Regional energy demand prediction processing method and electronic equipment

Abstract

The invention discloses a regional energy demand prediction processing method and electronic equipment. The method comprises the following steps: acquiring historical industrial data corresponding to a target area in a plurality of industrial dimensions; determining multiple groups of influence factor indexes influencing the energy demand; multiple energy prediction scenes are determined, and the multiple energy prediction scenes correspond to different carbon emission strategies; based on the historical industrial data corresponding to the plurality of industrial dimensions and the plurality of groups of influence factor indexes, adopting a long-term energy substitution planning system LEAP model to obtain energy demand prediction results corresponding to the target area in the plurality of energy prediction scenes, the source demand prediction result comprises an electric energy demand prediction result, a natural gas demand prediction result and a cold / heat load demand prediction result. According to the invention, the technical problems of lack of pertinence and low accuracy of energy demand prediction caused by incomplete consideration factors of regional energy demand prediction in related technologies are solved.

Description

Technical Field

[0001] The present invention relates to the field of energy demand, and in particular to a regional energy demand forecasting and processing method and electronic equipment. Background Art

[0002] Currently, the energy consumption structure of some regions (such as counties) is still primarily based on fossil fuels such as coal and oil. However, with growing environmental awareness and advances in renewable energy technologies, the proportion of clean energy in regional energy consumption is gradually increasing. However, existing methods for predicting regional energy consumption structures have some shortcomings. On the one hand, traditional forecasting models often focus on analyzing the consumption trends of a single energy type, ignoring the interrelationships and substitution effects between multiple energy sources, making it difficult to accurately grasp the overall evolution of the regional energy consumption structure. On the other hand, traditional forecasting methods do not fully consider other factors, resulting in significant deviations between the forecast results and actual conditions. In summary, the regional energy demand forecasting methods used in related technologies do not consider a comprehensive range of factors, resulting in problems such as a lack of targetedness and low accuracy.

[0003] To address the above-mentioned problems, no effective solutions have been proposed so far. Summary of the Invention

[0004] The embodiments of the present invention provide a regional energy demand forecasting processing method and electronic device to at least solve the technical problem in the related art that regional energy demand forecasting does not take into account all factors, resulting in a lack of pertinence and low accuracy in energy demand forecasting.

[0005] According to one aspect of an embodiment of the present invention, a regional energy demand forecasting and processing method is provided, including: obtaining historical industrial data corresponding to a target area in multiple industrial dimensions; determining multiple groups of influencing factor indicators that affect energy demand; determining multiple energy forecasting scenarios, wherein the multiple energy forecasting scenarios correspond to different carbon emission strategies; based on the historical industrial data corresponding to the multiple industrial dimensions, and the multiple groups of influencing factor indicators, a long-term energy substitution planning system LEAP model is used to obtain energy demand forecast results corresponding to the target area in the multiple energy forecasting scenarios, wherein the source demand forecast results include electricity demand forecast results, natural gas demand forecast results, and cold / heat load demand forecast results.

[0006] According to another aspect of an embodiment of the present invention, a regional energy demand forecasting and processing device is also provided, including: a data acquisition module for acquiring historical industrial data corresponding to a target area in multiple industrial dimensions; an indicator determination module for determining multiple groups of influencing factor indicators affecting energy demand; a scenario determination module for determining multiple energy forecasting scenarios, wherein multiple energy forecasting scenarios correspond to different carbon emission strategies; a demand forecasting module for obtaining energy demand forecast results corresponding to multiple energy forecasting scenarios of the target area based on the historical industrial data corresponding to multiple industrial dimensions and multiple groups of influencing factor indicators, using the LEAP model of the long-term energy substitution planning system, wherein the source demand forecast results include electricity demand forecast results, natural gas demand forecast results, and cold / heat load demand forecast results.

[0007] According to another aspect of an embodiment of the present invention, a non-volatile storage medium is provided. The non-volatile storage medium stores a plurality of instructions, and the instructions are suitable for being loaded and executed by a processor for any one of the regional energy demand forecasting processing methods.

[0008] According to another aspect of an embodiment of the present invention, an electronic device is also provided, including one or more processors and a memory, wherein the memory is used to store one or more programs, wherein when the one or more programs are executed by one or more processors, the one or more processors implement any one of the regional energy demand forecasting processing methods.

[0009] According to another aspect of an embodiment of the present invention, a computer program product is provided, including a computer program, which implements any one of the steps of the regional energy demand forecasting method when executed by a processor.

[0010] In an embodiment of the present invention, historical industrial data corresponding to multiple industrial dimensions of the target area are obtained; multiple groups of influencing factor indicators affecting energy demand are determined; multiple energy forecast scenarios are determined, wherein multiple energy forecast scenarios correspond to different carbon emission strategies; based on the historical industrial data corresponding to multiple industrial dimensions, and multiple groups of influencing factor indicators, the long-term energy substitution planning system LEAP model is used to obtain energy demand forecast results corresponding to multiple energy forecast scenarios of the target area, wherein the source demand forecast results include electricity demand forecast results, natural gas demand forecast results, and cold / heat load demand forecast results, thereby achieving the purpose of integrating historical development data of multiple industrial dimensions of the target area with multiple groups of influencing factor indicators, and using the long-term energy substitution planning system (LEAP model) to perform energy demand forecasting under different set carbon emission strategy scenarios, thereby achieving the technical effect of accurately predicting the demand for electricity, natural gas, and cold / heat loads, thereby solving the technical problem in the related technology that the regional energy demand forecast does not take into account comprehensive factors, resulting in a lack of pertinence and low accuracy in the energy demand forecast. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0012] Figure 1 is a flow chart of a regional energy demand forecasting method according to an embodiment of the present invention;

[0013] Figure 2 is a schematic diagram of an optional energy LEAP model prediction structure according to an embodiment of the present invention;

[0014] Figure 3 is an optional regional energy demand forecasting flow chart according to an embodiment of the present invention;

[0015] Figure 4 2 is a schematic diagram of a regional energy demand forecasting and processing device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0016] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0017] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0018] First, to facilitate understanding of the embodiments of the present invention, some of the terms or nouns involved in the present invention are explained below:

[0019] The Long-Rage Energy Alternatives Planning System (LEAP) model is an energy rule analysis and climate change assessment model developed by the Stockholm Institute. The LEAP model is established using the sectoral activity analysis method and is a bottom-up energy-environment accounting tool based on scenario analysis. The LEAP model includes links such as energy supply, energy processing and conversion, and terminal energy demand. It can be mainly used in energy planning or forecasting, energy balance sheets and environmental pollutant inventories, greenhouse gas emission reduction analysis, and comprehensive resource utilization planning. The LEAP model has a flexible and diverse structure. It can flexibly construct models and data structures based on the characteristics of the research object, the availability of data, and the purpose and type of analysis. It analyzes energy consumption and greenhouse gas emissions in different scenarios, from near to far in time and from small to large in space. It is applicable to areas within a large area of ​​a district.

[0020] According to an embodiment of the present invention, an embodiment of a method for regional energy demand forecasting processing is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0021] Figure 1 FIG. 1 is a flow chart of a method for predicting regional energy demand according to an embodiment of the present invention. Figure 1 As shown, the method includes the following steps:

[0022] Step S102: Obtain historical industry data corresponding to the target area in multiple industry dimensions;

[0023] Optionally, the target area may be, but is not limited to, a county area. The multiple industry dimensions may include, but are not limited to, agricultural production dimension, transportation dimension, tourism development dimension, and resident life dimension. Different industry dimensions have different industry characteristics, and historical industry data are acquired in a targeted manner according to the industry characteristics of each industry dimension. For example, the historical industry data corresponding to the agricultural production dimension may include, but are not limited to: the annual output value, growth rate, energy consumption per unit output value, etc. of each sub-industry in a preset historical period; the historical industry data corresponding to the transportation dimension may include, but are not limited to: the number of vehicles, growth rate, passenger volume, mileage, etc. in a preset historical period; the historical industry data corresponding to the tourism development dimension may include, but are not limited to: the energy consumption, growth rate, annual output value, etc. of the tourism industry output value in a preset historical period; the historical industry data corresponding to the resident life dimension may include, but are not limited to: the heating area, population size, population growth rate, etc. in a preset historical period.

[0024] Step S104, determining multiple groups of influencing factor indicators that affect energy demand;

[0025] Optionally, multiple groups of influencing factor indicators may include but are not limited to: regulatory factors, GDP, population and urbanization development factors (such as population growth rate), agricultural development factors (such as agricultural production scale, degree of agricultural modernization, status of cold chain logistics and warehousing industry of agricultural products, etc.), tourism development factors (such as tourism development scale, infrastructure conditions for tourism development, etc.), industrial development factors (such as gross industrial output value, proportion of gross industrial output value, etc.), technology application factors (which can be measured through electricity consumption for grain production, electricity consumption for agricultural and sideline product processing, etc.), number of electrical appliances, electric vehicle development factors (such as purchasing power of micro and small electric vehicles), etc.

[0026] It's important to note that different influencing factor indicators can reveal the underlying mechanisms of energy demand changes, helping the model more accurately predict energy demand across various industry dimensions under specific scenarios, including electricity, natural gas, and cooling / heating loads. By setting different groups of influencing factor indicators, it's possible to simulate and compare a variety of possible future scenarios, including changes in carbon emission strategies. This helps identify the factors most critical to energy demand and thus guide the development of energy management strategies.

[0027] Step S106, determining multiple energy forecast scenarios, wherein the multiple energy forecast scenarios correspond to different carbon emission strategies;

[0028] Optionally, multiple energy forecast scenarios can be set, each corresponding to a different carbon emission strategy. For example, three energy forecast scenarios can be constructed based on different carbon emission strategies: a clean scenario, a low-carbon scenario, and a zero-carbon scenario. These scenarios can be used to analyze the future consumption of various types of energy in the county under different scenarios.

[0029] The Clean Scenario describes how energy demand and supply will develop naturally without new regulatory intervention or technological change. There will be no regulatory intervention, no set development targets or boundaries, and slow growth in new energy capacity and county electrification levels.

[0030] Low-carbon scenario: This refers to the possible future development path of county-level regions based on the baseline scenario, combined with published rules and documents. For example, it is assumed that the implementation of certain rules will promote steady growth in county-level new energy installed capacity, stable electrification levels, and basically achieve low-carbon energy consumption in the county by the end of the year.

[0031] Zero carbon scenario: This scenario builds on the two aforementioned scenarios, placing greater emphasis on energy conservation and environmental protection, while also incorporating various regulatory documents. For example, the implementation of certain regulations could lead to rapid growth in county-level renewable energy capacity and electrification, ultimately achieving zero carbon consumption in the county by the end of the year. The basic parameters for each scenario are shown in Table 1.

[0032] Table 1 Basic parameter indicators of three energy forecast scenarios

[0033]

[0034]

[0035] Step S108, based on the historical industrial data corresponding to multiple industrial dimensions and multiple groups of influencing factor indicators, the long-term energy substitution planning system LEAP model is used to obtain the energy demand forecast results corresponding to multiple energy forecast scenarios for the target area, where the source demand forecast results include electricity demand forecast results, natural gas demand forecast results, and cooling / heating load demand forecast results.

[0036] Alternatively, by integrating historical data from multiple industry dimensions in the target region with multiple sets of detailed influencing factor indicators, the Long-Term Energy Alternative Planning System (LEAP model) can be used to dynamically forecast energy demand for electricity, natural gas, and cooling / heating loads under different carbon emission strategy scenarios. This allows for a comprehensive and accurate understanding of regional energy demand, providing strong data analysis and decision-making support for formulating energy development strategies adapted to low-carbon transitions. This innovative approach combines historical industry data with multi-dimensional influencing factors, enhancing the realism and flexibility of forecasts and facilitating more scientific and reasonable energy planning in the face of uncertainty and variability.

[0037] Optionally, the regional energy demand forecasting and processing method of this embodiment is based on the LEAP model, which divides the terminal energy demand of a region (such as a county) into four dimensions: agriculture, industry, services, and residents' lives. By inputting data from each dimension, the energy demand in different scenarios and years is analyzed. Figure 2This is a schematic diagram of an optional energy LEAP model prediction structure according to an embodiment of the present invention. The agricultural production dimension primarily encompasses activities such as agricultural production, processing, and storage. Energy consumption in this sector is primarily reflected in electricity, heating, and cooling. Agricultural production, including the use of agricultural machinery and irrigation systems, consumes significant amounts of electricity; processing and storage also require significant amounts of heating and cooling energy. The industrial production dimension encompasses multiple subsectors, including manufacturing, construction, and mining. Energy consumption is primarily in electricity and heating. Manufacturing equipment and industrial park infrastructure operations require significant electricity. Furthermore, heating and cooling processes in industrial production also consume significant amounts of heat and cooling energy. The service industry development dimension, encompassing commercial services, tourism, and other service industries, primarily consumes electricity and heating and cooling energy. Commercial building lighting and air conditioning systems in commercial services require significant electricity; tourism development involves energy consumption in hotels and scenic area facilities, such as hot water supply and heating. The residential living dimension encompasses daily lighting, air conditioning, transportation, heating, and laundry equipment. Energy consumption covers various forms such as electricity, heating and cooling, and natural gas. Daily lighting and the use of household appliances are the main parts of electricity consumption; air conditioning and heating equipment consume energy such as electricity and natural gas respectively; charging of electric vehicles during transportation is also closely related to electricity consumption.

[0038] In an optional embodiment, based on historical industrial data corresponding to multiple industrial dimensions and multiple groups of influencing factor indicators, the long-term energy substitution planning system LEAP model is used to obtain energy demand forecast results corresponding to multiple energy forecast scenarios for the target area, including: based on historical industrial data corresponding to multiple industrial dimensions and multiple groups of influencing factor indicators, the LEAP model is used to predict the industrial energy forecast results corresponding to multiple industrial dimensions under any energy forecast scenario, wherein the industrial energy forecast results include one or more of the electricity demand forecast results, natural gas demand forecast results, and cold / heat load demand forecast results corresponding to the industry; the industrial energy forecast results corresponding to multiple industrial dimensions are summed up to obtain the energy demand forecast result corresponding to any energy forecast scenario; the energy demand forecast results corresponding to multiple energy forecast scenarios are obtained by using the method of obtaining the energy demand forecast result corresponding to any energy forecast scenario.

[0039] Optionally, the LEAP model can process historical industry data and multiple sets of influencing factor indicators for each industry dimension (such as agricultural production, industrial production, service industry development, and residents' daily lives) within any energy forecast scenario. This allows the model to provide more refined demand forecasts tailored to the specific characteristics of different industries. Under different energy forecast scenarios, the LEAP model can predict one or more of the following: electricity demand, natural gas demand, and cooling / heating load demand corresponding to a specific industry. This step leverages the model's built-in industry analysis capabilities, analyzing historical data and influencing factors to predict future energy demand changes for each industry under a specific scenario. Summing the energy forecast results across different industry dimensions yields a comprehensive energy demand forecast for the entire target region under any energy forecast scenario. This process effectively integrates forecast data across various industries, providing a comprehensive perspective on energy demand forecasting for the entire region. By repeating this process—predicting and summing energy demand under different energy forecast scenarios (such as clean energy, low-carbon energy, and zero-carbon energy)—energy demand forecasts for multiple scenarios can be obtained. This multi-scenario analysis method helps to comprehensively evaluate the impact of different carbon emission strategies on energy demand, thereby providing empirical support for the formulation of more reasonable and effective energy strategies.

[0040] In this embodiment, by integrating the historical industrial data of the target area and multiple groups of influencing factor indicators into the LEAP model, the energy demand of each industry dimension under different energy scenarios is predicted respectively. By summarizing and calculating these prediction results, a comprehensive energy demand forecast for the entire target area under multiple energy forecast scenarios is systematically generated, thereby providing more detailed and comprehensive forecast data support for regional energy planning.

[0041] In an optional embodiment, based on historical industrial data corresponding to multiple industrial dimensions and multiple groups of influencing factor indicators, the LEAP model is used to predict the industrial energy forecast results corresponding to multiple industrial dimensions under any energy forecast scenario, including: based on historical industrial data corresponding to multiple industrial dimensions and multiple groups of influencing factor indicators, the data analysis model built into the LEAP model corresponding to multiple industrial dimensions is used to predict the industrial energy forecast results corresponding to multiple industrial dimensions under any energy forecast scenario, wherein the multiple industrial dimensions include agricultural production dimension, transportation dimension, tourism development dimension, and resident life dimension.

[0042] Optionally, the LEAP model can leverage built-in data analysis models for different industry dimensions (such as agricultural production, transportation, tourism development, and residents' livelihoods), combined with historical industry data for the target region and multiple sets of influencing factor indicators, to accurately predict energy demand for each industry dimension under any given energy forecast scenario. In other words, the LEAP model in this embodiment has the ability to be applied to specific industries and perform scenario analysis, enabling a more detailed assessment of demand changes across different industries during the energy transition process, providing a scientific basis for developing targeted energy strategies.

[0043] In an optional embodiment, the data analysis model corresponding to the agricultural production dimension built into the LEAP model is used to obtain the power demand forecast result corresponding to the agricultural production dimension in the following manner:

[0044]

[0045] in, represents the electricity demand forecast result of agricultural production dimension in year t; is the electricity load in the tth year of agricultural product planting; is the electricity load of agricultural product processing in year t; is the electricity load of other parts of the agricultural production dimension in year t, where t is the preset forecast year;

[0046] The cooling / heating load demand forecast results corresponding to the agricultural production dimension are obtained in the following way:

[0047]

[0048] in, represents the cooling / heating load demand forecast result of the agricultural production dimension in year t; is the cooling / heating demand of the warehousing link in year t, where is the total warehouse area of ​​the target area planned in year t; is the growth coefficient of warehouse construction area in the target area; The cooling / heating demand per square meter of the warehouse; is the cooling / heating demand of other parts of agricultural production dimension in year t.

[0049] Optionally, the electricity load for agricultural production in year t can include, but is not limited to, the electricity demand from the operation of agricultural machinery, irrigation systems, greenhouses, and other agricultural facilities. The electricity load for agricultural processing in year t can include, but is not limited to, operations such as cleaning, cutting, packaging, and storage, particularly automated equipment and cold chain logistics systems in food processing. The electricity load for the rest of the agricultural production dimension in year t can include, but is not limited to, other agricultural production activities beyond planting and processing, such as lighting in agricultural buildings and the operation of agricultural production management facilities. The cooling / heating demand for storage in year t refers to the cooling / heating energy required for post-harvest storage and preservation of agricultural products. Therefore, this demand is closely related to the warehouse area, the growth coefficient of warehouse construction area, and the cooling / heating demand per unit of warehouse area. The cooling / heating demand for the rest of the agricultural production dimension in year t can include, but is not limited to, cooling / heating energy consumption for agricultural product transportation and agricultural production support facilities (such as freezers and greenhouse temperature control).

[0050] Alternatively, incorporating an analysis of key agricultural development factors, such as the construction of high-standard farmland, the electrification of agricultural machinery, and the development of cold-chain logistics and warehousing for agricultural products, can provide a more specific understanding of the application scenarios of these forecasting models. For example, as the electrification rate of agricultural machinery increases, electricity demand for agricultural product processing and other areas will significantly increase, reflecting the increasing demand for electricity in the process of agricultural modernization. Furthermore, with the development of cold-chain logistics and warehousing for agricultural products, the demand for cooling and heating in the warehousing process will also increase, reflecting the impact of agricultural product storage and preservation on cooling and heating load requirements.

[0051] The LEAP model’s built-in data analysis model, which corresponds to the agricultural production dimension, can comprehensively consider the above factors and more details, thereby obtaining more accurate electricity demand forecast results and cooling / heating load demand forecast results.

[0052] In an optional embodiment, the data analysis model corresponding to the transportation dimension built into the LEAP model is used to obtain the power demand forecast result corresponding to the transportation dimension in the following manner:

[0053]

[0054] in, represents the forecast result of electricity demand in the transportation dimension in year t, is the number of private cars in the target area in year t, is the number of buses in the target area in year t, is the average annual electricity demand of private cars, is the average annual electricity demand of buses, is the electricity load of other parts of the transportation dimension in year t.

[0055] Optionally, the electricity demand forecast results for the transportation dimension in the forecast year (t) can reflect the energy consumption of all vehicles and related facilities in this area. The number of private cars and the average annual electricity demand of private cars in the target area in year (t). With the popularization of electric vehicles, the energy demand of private cars will shift from traditional fossil fuels to electricity, and this demand will increase significantly. The number of buses and the average annual electricity demand of buses in the target area in year (t). Similarly, the promotion and use of electric buses will also have a significant impact on the electricity demand of the transportation dimension. The electricity load of other parts of the transportation dimension in year (t), except for private cars and buses, includes other electric vehicles such as electric bicycles, electric motorcycles, and electric trucks, as well as the electricity consumption related to charging station infrastructure and traffic signal systems.

[0056] The LEAP model's built-in transportation data analysis model, combined with key indicators such as the growth in the number of private cars and buses in a target area (such as a county), the penetration rate of electric vehicles, and the average annual electricity demand, can accurately calculate the electricity demand forecast results for the transportation dimension in different years.

[0057] In an optional embodiment, the data analysis model corresponding to the tourism development dimension built into the LEAP model is used to obtain the power demand forecast result corresponding to the tourism development dimension in the following manner:

[0058]

[0059] in, represents the forecast result of electricity demand in tourism development dimension in year t; is the electricity load in the tth year of the house construction phase; is the electricity load of the accommodation link in year t; is the electricity load of other parts of tourism development dimension in year t;

[0060] The natural gas demand forecast results corresponding to the tourism development dimension are obtained in the following way:

[0061]

[0062] in, represents the natural gas demand forecast result of tourism development dimension in year t; is the natural gas demand of the catering sector in year t; is the natural gas demand in year t for other parts of the tourism development dimension;

[0063] The cooling / heating load demand forecast results corresponding to the tourism development dimension are obtained in the following way:

[0064]

[0065] in, represents the cooling / heating load demand forecast result of tourism development dimension in year t; is the cooling / heating demand of the accommodation link in year t, where The number of rural tourists in the study area in year t that is pre-planned for the target area; β tourist is the growth coefficient of the number of rural tourists in the target area; The average cooling / heating demand per rural tourist in the target area; is the cooling / heating demand of other parts of the agricultural production sector in year t.

[0066] Optionally, the electricity demand forecast results for the tourism development dimension cover all aspects of tourism activities related to electricity consumption, including but not limited to electricity consumption in buildings, electricity demand in accommodation facilities, and electricity consumption in other tourism service facilities. Regarding the electricity load for housing construction and accommodation in the forecast year (t), the expansion of the tourism industry is usually accompanied by the construction and renovation of tourism facilities, which directly affects the increase in electricity demand, especially for lighting, air conditioning, and hot water supply in accommodation facilities. The electricity load for other parts of the tourism development dimension, excluding housing construction and accommodation, in the forecast year (t), can include, but is not limited to, electricity consumption in the management of tourist attractions, the operation of entertainment facilities, and the sale of tourist products.

[0067] Alternatively, in the tourism industry, natural gas is primarily used for cooking food in catering services and heating some accommodation facilities. Natural gas demand in the catering sector in year (t) reflects the increasing demand for catering services and, consequently, increased natural gas consumption as the number of tourists increases. Natural gas demand for sectors other than catering within the tourism development dimension may include, but is not limited to, the heating demand of some accommodation facilities using non-electric heating.

[0068] Optionally, the cooling / heating load demand forecast for the tourism development dimension covers all aspects of tourism activities related to cooling / heating energy consumption. The cooling / heating demand for accommodation in year (t) takes into account the growth in the number of tourists and the cooling / heating energy requirements per tourist, used to predict the energy demand for cooling and heating in accommodation facilities. The cooling / heating demand for all components of the tourism development dimension, excluding the accommodation sector, in year (t) is also included.

[0069] It's important to note that with the rapid development and improvement of county tourism, demand for electricity and natural gas has increased significantly, particularly in housing construction, accommodation, and catering services. The LEAP model takes these factors into account when forecasting energy demand in the tourism development dimension. By inputting key indicators such as historical tourism industry data, visitor numbers, visitor growth trends, and per capita energy consumption, it accurately predicts electricity demand, natural gas demand, and cooling / heating load demand in the tourism development dimension across different years.

[0070] In an optional embodiment, the data analysis model corresponding to the resident living dimension built into the LEAP model is used to obtain the power demand forecast result corresponding to the resident living dimension in the following manner:

[0071]

[0072] in, represents the electricity demand forecast result of the residents’ living dimension in year t, is the electric energy load for daily lighting in year t, are the electricity load of washing in year t, is the electricity load of other parts of the residents' living dimension in year t;

[0073] The natural gas demand forecast results corresponding to the residents' living dimensions are obtained in the following way:

[0074]

[0075] in, represents the natural gas demand forecast result of the tth year in the residents’ living dimension, is the natural gas demand for cooking in year t, is the natural gas demand in the other parts of the residential living dimension in year t;

[0076] The cooling / heating load demand forecast results corresponding to the residents' living dimensions are obtained by the following method:

[0077]

[0078] in, represents the cooling / heating load demand forecast result of the tth year in the residents’ living dimension, is the cooling / heating demand of the air conditioner in year t, is the cooling / heating demand of heating in year t; is the cooling / heating demand of other parts of residents’ living dimensions in year t.

[0079] Optionally, the electricity demand forecast for the resident life dimension in the forecast year (t) integrates all electricity consumption involved in daily life, including but not limited to lighting, washing, and the operation of household appliances. The electricity load for daily lighting and washing in year (t) is the most basic source of electricity consumption in residents' daily lives. With the improvement of living standards and the increase in the penetration rate of electrical appliances, the electricity demand for these two components will increase accordingly. The electricity load for other components of the resident life dimension besides lighting and washing can include, but is not limited to, the power consumption of other household appliances and facilities such as televisions, refrigerators, washing machines, air conditioners, and electric vehicle charging.

[0080] The optional natural gas demand forecast for the residential living dimension in forecast year (t) primarily reflects residents' demand for cooking and other activities that may use natural gas. With urbanization and improved living standards, more and more residents are choosing natural gas as a cooking energy source, and natural gas demand for cooking in year (t) will also increase significantly. Natural gas demand for other activities in the residential living dimension, beyond cooking, can include, but is not limited to, energy consumption in non-electric heating modes such as gas water heaters, fireplaces, and certain types of heating equipment.

[0081] Optionally, the cooling / heating load demand forecast for the residential living dimension in the forecast year (t) includes seasonal regulation needs such as air conditioning and heating. With climate change and improved living standards, the frequency and duration of use of air conditioning and heating systems are increasing, leading to increased cooling / heating load demand. Cooling / heating demand for other components of the residential living dimension, beyond air conditioning and heating, can include, but is not limited to, the cooling and heating energy demand for other living services such as hot water supply and food storage.

[0082] Optionally, the cooling / heating demand of air conditioning in year t and the cooling / heating demand of heating in year t may be obtained by, but is not limited to, the following method:

[0083]

[0084] in, is the currently known number of rural population in the target area in year t; β village are the growth coefficients of rural population in the target area; are the per capita cooling / heating demands in rural areas of the target area, respectively.

[0085] It's important to note that with the continued rise in county incomes and consumption upgrades, demand for electricity, natural gas, and cooling / heating loads in residential areas is on the rise, especially with the increasing ownership of appliances like air conditioners, refrigerators, and washing machines. The LEAP model, through its built-in data analysis model, accurately predicts energy demand in these areas based on historical data, such as the number of appliances per 100 households and the penetration rate of electric vehicles, as well as potential future changes, such as population growth, urbanization rate, and electrification levels.

[0086] In an optional embodiment, when the multiple industry dimensions also include an industrial and commercial dimension, the data analysis model corresponding to the industrial and commercial dimension built into the LEAP model is used to obtain the power demand forecast result corresponding to the industrial and commercial dimension in the following manner:

[0087]

[0088] in, is the electricity demand forecast result for the industrial and commercial dimension in year t, is the electric energy load of the smelting process in year t, is the electric energy load of the electrolysis link in year t, is the electricity load of other parts of the industrial and commercial dimension in year t;

[0089] The natural gas demand forecast results corresponding to the industrial and commercial dimensions are obtained in the following way:

[0090]

[0091] in, is the natural gas demand forecast result for the industrial and commercial dimension in year t, is the natural gas demand of industrial and commercial users in year t; is the natural gas demand in year t for other parts of the industrial and commercial dimension;

[0092] The cooling / heating load demand forecast results corresponding to the industrial and commercial dimensions can be obtained by the following method:

[0093]

[0094] in, is the cooling / heating load demand forecast result for the industrial and commercial dimension in year t; is the cooling / heating demand of the accommodation link in year t, where is the total output of the target area in the tth year planned in advance; β tourist is the production capacity growth coefficient of the target area; The cooling / heating demand per unit capacity of the target area; is the cooling / heating demand in the process reduction phase in year t; is the cooling / heating demand of other parts of the industrial and commercial dimension in year t.

[0095] Optionally, the electricity demand forecast for the industrial and commercial dimension in the forecast year (t) can reflect the total electricity consumption of all industrial and commercial activities. Smelting and electrolysis are two of the major industrial energy consumers, especially in the metallurgical industry and some chemical production processes, which have extremely high electricity demand. Therefore, the electricity load of these two sectors in year (t) is considered separately. The electricity load of other sectors in the industrial and commercial dimension, excluding smelting and electrolysis, can include, but is not limited to, electricity consumption in commercial activities and indirect demand for electricity from other industrial energy consumption.

[0096] Optionally, the natural gas demand forecast for the commercial and industrial sector in forecast year (t) covers both direct natural gas demand from commercial and industrial users and other activities within the commercial and industrial sector that may involve natural gas consumption. Natural gas demand from commercial and industrial users in year (t) can refer to direct use of natural gas for heating, cooling, or as a feedstock in industrial and commercial facilities. Natural gas demand beyond direct use by commercial and industrial users within the commercial and industrial sector can include, but is not limited to, indirect natural gas demand, such as electricity generated by natural gas-fired power plants for use in commercial and industrial activities.

[0097] Optionally, the cooling / heating load demand forecast for the commercial and industrial dimension for forecast year (t) includes the direct cooling / heating demand forecast for industrial and commercial activities. The cooling / heating demand for year (t) includes air conditioning and process recovery (or other industrial processes requiring cooling / heating energy). The latter specifically emphasizes the high cooling / heating demand for production processes, such as certain chemical and food processing industries. In addition to the cooling / heating demand for other components mentioned above, the commercial and industrial dimension can also include cooling / heating demand for commercial buildings, industrial waste heat recovery, and other dimensions.

[0098] In this embodiment, specifically, the LEAP model can predict changes in energy demand in the industrial and commercial sectors under different scenarios such as clean, low-carbon or zero-carbonization based on historical industrial data, such as industrial added value and commercial electricity consumption, combined with possible future regulatory orientations, GDP growth rates, technological progress and population changes, so as to achieve accurate prediction of energy demand in the industrial and commercial dimensions in a specific year (t).

[0099] In an optional embodiment, after obtaining energy demand forecast results corresponding to multiple energy forecast scenarios for the target area based on historical industry data corresponding to multiple industry dimensions and multiple groups of influencing factor indicators using the LEAP model of the long-term energy alternative planning system, the method further includes: determining the energy development strategies corresponding to the multiple energy forecast scenarios for the target area based on the energy demand forecast results corresponding to the multiple energy forecast scenarios.

[0100] Optionally, once the LEAP model has generated energy demand forecasts for the target region under different energy scenarios, the next key step is to analyze these forecasts and develop appropriate energy development strategies based on them. These strategies aim to guide the target region on how to effectively meet energy demand under the given scenarios while optimizing its energy consumption structure and minimizing environmental impact.

[0101] It's important to note that by constructing multiple energy scenarios (such as clean energy, low-carbon energy, and zero-carbon energy), the LEAP model can predict energy demand in target regions under different future policies and technology pathways. These forecasts are not just theoretical analysis; they also provide a solid foundation for developing targeted energy development strategies.

[0102] In an optional embodiment, when the energy development strategy includes an energy structure adjustment strategy and an energy scheduling strategy, based on the energy demand forecast results corresponding to multiple energy forecast scenarios, the energy development strategies corresponding to the target area in multiple energy forecast scenarios are determined, including: comparing the differences in the electricity demand forecast results, natural gas demand forecast results, and cold / heat load demand forecast results corresponding to any energy forecast scenario, identifying the target energy and peak demand period required under any energy forecast scenario, wherein the target energy is the main demand energy of the target area under the corresponding energy forecast scenario; determining the energy structure adjustment strategy corresponding to any energy forecast scenario based on the demand forecast results of the target energy; determining the energy scheduling strategy corresponding to any energy forecast scenario based on the peak demand period; and obtaining the energy development strategies corresponding to multiple energy forecast scenarios by adopting the method of obtaining the energy development strategy corresponding to any energy forecast scenario.

[0103] Optionally, the target energy is the key driver of energy demand, and the target energy can be any one of electricity, natural gas, and cooling / heating load. By comparing and analyzing the differences in electricity demand forecast results, natural gas demand forecast results, and cooling / heating load demand forecast results under various energy forecast scenarios, the key drivers of energy demand and peak demand periods under different scenarios are identified. Based on the key drivers of the energy demand forecast results, corresponding energy structure adjustment strategies are formulated, including but not limited to increasing the proportion of renewable energy, optimizing the efficiency of traditional energy utilization, and promoting the transformation of energy consumption patterns. For peak demand periods, energy supply and demand balancing mechanisms are designed, such as implementing demand-side management, promoting energy storage technology, optimizing power dispatching, and other energy dispatching strategies to ensure the stability and security of energy supply.

[0104] Specifically, for each energy forecast scenario (clean, low-carbon, and zero-carbon), the LEAP model will forecast detailed electricity demand, natural gas demand, and cooling / heating load demand. By comparing these forecasts, we can identify demand differences under each scenario: which energy sources will experience significant increases or decreases, and the specific time periods during which these changes are likely to occur (e.g., peak electricity demand in summer, peak heating in winter). This allows us to determine the target energy source for each scenario—the energy type with the highest demand or the most impact in that particular scenario. Once the target energy source for each scenario is determined, the next step is to develop an energy mix adjustment strategy to optimize the match between energy supply and demand. For example, if the electricity demand forecast under the clean scenario indicates a significant increase, energy mix adjustment strategies may include, but are not limited to: expanding renewable energy generation capacity, such as wind and solar power, to increase clean energy supply; strengthening grid construction and intelligent upgrades to enhance the grid's carrying capacity and dispatch flexibility; and promoting electrification of energy consumption, particularly in industry, transportation, and residential sectors, to reduce reliance on fossil fuels. Similarly, for scenarios where demand for natural gas or cooling / heating energy increases significantly, adjustment strategies may include, but are not limited to, increasing natural gas pipeline infrastructure, increasing the use of heat pumps and thermal storage technologies, or developing distributed energy systems.

[0105] Energy dispatch strategies aim to ensure a stable energy supply during peak demand periods. By analyzing forecasts for electricity, natural gas, and cooling / heating loads, peak demand periods can be identified. For example, electricity demand may peak during high summer temperatures, while natural gas and heating load demand may surge during cold winter weather. Energy dispatch strategies for these peak periods may include, but are not limited to, establishing demand response mechanisms to encourage users to reduce non-essential energy consumption during peak periods or adopting measures such as staggered electricity use. They may also include increasing backup energy supply capacity, such as building emergency generators or stockpiling additional natural gas or cooling / heating capacity. Energy storage technologies, such as batteries and thermal storage, can be used to smooth demand fluctuations and ensure sufficient energy reserves during peak periods. Ultimately, the energy mix adjustment strategies and energy dispatch strategies described above, based on a single energy forecast scenario, can be integrated to form a comprehensive energy development strategy tailored to different scenarios.

[0106] Through the above steps S102 to S108, the purpose of integrating historical development data of multiple industrial dimensions and multiple groups of influencing factor indicators in the target area can be achieved, and the long-term energy alternative planning system (LEAP model) can be used to perform simulation and prediction under different set carbon emission strategy scenarios, thereby achieving accurate prediction of electricity, natural gas and cooling / heating load demand, and thus providing scientific basis and technical support for formulating energy development strategies that can both meet energy demand and achieve carbon emission reduction goals, thereby solving the technical problem that regional energy demand prediction in related technologies is not comprehensive, resulting in a lack of targetedness and low accuracy in energy demand prediction.

[0107] Based on the above embodiment and optional embodiment, the present invention proposes an optional implementation method of a regional energy demand forecasting method, which includes:

[0108] S1: Construction of the LEAP energy demand prediction model. Based on the LEAP model, the terminal energy demand of a region (such as a county) is divided into four dimensions: agriculture, industry, service industry, and residents' life. By inputting data from each dimension, the energy demand in different scenarios and years is analyzed. The specific construction process and model information of the LEAP prediction model are the same as those in the previous embodiment and will not be repeated here. Figure 3 This is an optional regional energy demand forecasting flow chart according to an embodiment of the present invention. Taking a county as an example, the forecasting steps are as follows:

[0109] (1) Data collection: By consulting the data, we collected detailed data on resource endowments, relevant regulations, economic development, and industrial conditions in rural areas.

[0110] (2) Data input. The collected data are sequentially input into the constructed LEAP model framework. In the agricultural production dimension, the annual output value, growth rate, and energy consumption per unit of output value of each sub-industry are input; in the transportation dimension, the number of vehicles, growth rate, passenger volume, and mileage are input; in the tourism development dimension, the energy consumption, growth rate, and annual output value of the tourism industry are input; in the residents’ living dimension, the heating area, population size, and population growth rate are input.

[0111] (3) Scenario design. Based on the basic scenario, design different energy forecast scenarios such as acceleration scenario and carbon emission reduction scenario. Consider the impact of possible alternative results on possible future events, and design different development environment assumptions and development rate assumptions under different scenarios.

[0112] (4) Data Analysis. LEAP's built-in data analysis model is used to calculate forecasts for electricity, gas, and heating and cooling loads. The results page allows users to view the growth of electricity, gas, heating and cooling loads over time under different scenarios, as well as the distribution of hydrogen loads across sectors under different scenarios. The energy balance interface allows users to view the energy balance sheet and analyze the flow and conversion of electricity, natural gas, heating and cooling energy.

[0113] (5) Results summary. Organize the data analysis results and draw electricity, gas, cooling and heating load development charts, department-specific electricity, gas, cooling and heating load charts, etc. Summarize the electricity, gas, cooling and heating load development forecasts under different scenarios.

[0114] (6) Result analysis: Based on the development rates of electricity, gas, cooling and heating loads under different scenarios in the forecast results, relevant regional energy development strategies are formulated.

[0115] S2: Determine the factors affecting regional energy demand for analysis, which may include but are not limited to: regulatory factors, GDP, population and urbanization development factors (such as population growth rate), agricultural development factors (such as agricultural production scale, degree of agricultural modernization, cold chain logistics and warehousing industry status of agricultural products, etc.), tourism development factors (such as tourism development scale, tourism development infrastructure conditions, etc.), industrial development factors (such as industrial output value, proportion of industrial output value, etc.), technology application factors (which can be measured through electricity consumption for grain production, electricity consumption for agricultural and sideline product processing, etc.), number of electrical appliances, electric vehicle development factors (such as purchasing power for micro and small electric vehicles), etc.

[0116] S3: Construction of county-level energy forecast scenarios, specifically:

[0117] Multiple energy forecast scenarios are set, and each energy forecast scenario corresponds to a different carbon emission strategy. For example, according to different carbon emission strategies, three energy forecast scenarios can be constructed: a clean scenario, a low-carbon scenario, and a zero-carbon scenario. These scenarios are used to analyze the future consumption of various types of energy in counties under different scenarios. The basic parameter indicators of each forecast scenario are the same as those in the previous embodiment and will not be repeated here.

[0118] S4: Modeling of county-level electricity, gas, and cooling / heating load consumption. The specific modeling process is the same as that in the previous embodiment and will not be repeated here.

[0119] It should be noted that the regional energy demand forecasting and processing method of this embodiment is equivalent to an energy consumption structure forecasting method for a specific region (such as a county). This method can accurately predict the consumption trends of various energy sources such as electricity, gas, heat and cooling in a specific region, so as to promote the optimization of the regional energy structure and provide support for promoting the rational use of energy in specific regions.

[0120] This embodiment also provides a regional energy demand forecasting and processing device for implementing the aforementioned embodiments and preferred implementations. Details already described are omitted for clarity. As used below, the terms "module" and "device" may refer to a combination of software and / or hardware that implements a predetermined function. While the devices described in the following embodiments are preferably implemented in software, implementation using hardware, or a combination of software and hardware, is also possible and contemplated.

[0121] According to an embodiment of the present invention, there is also provided an embodiment of a device for implementing the above-mentioned regional energy demand forecasting method. Figure 4 FIG. 1 is a structural diagram of a regional energy demand forecasting and processing device according to an embodiment of the present invention. Figure 4 As shown, the regional energy demand forecasting and processing device includes: a data acquisition module 400, an indicator determination module 402, a scenario determination module 404, and a demand forecasting module 406, wherein:

[0122] The data acquisition module 400 is used to obtain historical industry data corresponding to multiple industry dimensions of the target area;

[0123] An indicator determination module 402, connected to the data acquisition module 400, is used to determine multiple groups of influencing factor indicators that affect energy demand;

[0124] A scenario determination module 404, connected to the indicator determination module 402, is used to determine a plurality of energy forecast scenarios, wherein the plurality of energy forecast scenarios correspond to different carbon emission strategies;

[0125] The demand forecasting module 406 is connected to the scenario determination module 404 and is used to obtain energy demand forecast results corresponding to multiple energy forecast scenarios for the target area based on historical industry data corresponding to multiple industry dimensions and multiple groups of influencing factor indicators, using the long-term energy alternative planning system LEAP model. Among them, the source demand forecast results include electricity demand forecast results, natural gas demand forecast results, and cooling / heating load demand forecast results.

[0126] It should be noted that the above modules can be implemented by software or hardware. For example, for the latter, it can be implemented in the following ways: the above modules can be located in the same processor; or the above modules can be located in different processors in any combination.

[0127] It should be noted that the data acquisition module 400, indicator determination module 402, scenario determination module 404, and demand forecasting module 406 correspond to steps S102 to S108 in the embodiment. The examples and application scenarios implemented by these modules and corresponding steps are the same, but are not limited to the contents disclosed in the above embodiment. It should be noted that the above modules, as part of the device, can be run on a computer terminal.

[0128] It should be noted that the optional or preferred implementation of this embodiment can be found in the relevant description in the embodiment, which will not be repeated here.

[0129] The above-mentioned regional energy demand forecasting and processing device may also include a processor and a memory. The above-mentioned data acquisition module 400, indicator determination module 402, scenario determination module 404, demand forecasting module 406, etc. are all stored in the memory as program modules, and the processor executes the above-mentioned program modules stored in the memory to realize the corresponding functions.

[0130] The processor includes a core, which retrieves corresponding program modules from memory. There can be one or more cores. Memory may include non-permanent memory in a computer-readable medium, random access memory (RAM), and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory includes at least one memory chip.

[0131] According to an embodiment of the present application, an embodiment of a non-volatile storage medium is also provided. Optionally, in this embodiment, the non-volatile storage medium includes a stored program, wherein, when the program is executed, the device containing the non-volatile storage medium is controlled to execute any of the above-mentioned regional energy demand forecasting and processing methods.

[0132] Optionally, in this embodiment, the non-volatile storage medium may be located in any computer terminal in a computer terminal group in a computer network, or in any mobile terminal in a mobile terminal group, and the non-volatile storage medium includes a stored program.

[0133] Optionally, a program that controls a device where a non-volatile storage medium is located to execute any one of the steps of the above-mentioned regional energy demand forecasting processing method when the program is running.

[0134] According to an embodiment of the present application, a processor embodiment is further provided. Optionally, in this embodiment, the processor is configured to run a program, wherein the program, when running, executes any of the above-mentioned regional energy demand forecasting methods.

[0135] According to an embodiment of the present application, an embodiment of a computer program product is also provided, which, when executed on a data processing device, is suitable for executing a program that initializes any one of the steps of the above-mentioned regional energy demand forecasting processing method.

[0136] An embodiment of the present invention provides an electronic device, which includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, the program implements any one of the steps of the above-mentioned regional energy demand forecasting processing method.

[0137] The above sequence of the embodiments of the present invention is for description only and does not represent the superiority or inferiority of the embodiments.

[0138] In the above embodiments of the present invention, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0139] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only exemplary. For example, the division of the above modules can be a logical function division. In actual implementation, there may be other division methods, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, modules or indirect coupling or communication connection of modules, which can be electrical or other forms.

[0140] The modules described above as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed across multiple modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment.

[0141] In addition, the functional modules in various embodiments of the present invention may be integrated into a single processing module, or each module may exist physically separately, or two or more modules may be integrated into a single module. The aforementioned integrated modules may be implemented in the form of hardware or software functional modules.

[0142] If the above-mentioned integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can be stored in a computer-readable non-volatile storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a non-volatile storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present invention. The aforementioned non-volatile storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk, and other media that can store program codes.

[0143] The above are only preferred embodiments of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A regional energy demand forecasting method, characterized in that: include: Obtain historical industry data corresponding to multiple industry dimensions in the target area; Identify multiple groups of influencing factor indicators that affect energy demand; determining a plurality of energy forecast scenarios, wherein the plurality of energy forecast scenarios correspond to different carbon emission strategies; Based on the historical industrial data corresponding to the multiple industrial dimensions and the multiple groups of influencing factor indicators, the long-term energy alternative planning system LEAP model is used to obtain the energy demand forecast results corresponding to the multiple energy forecast scenarios for the target area, wherein the source demand forecast results include electricity demand forecast results, natural gas demand forecast results, and cold / heat load demand forecast results.

2. The method according to claim 1, characterized in that The energy demand forecast results corresponding to the target area in the multiple energy forecast scenarios are obtained by using the long-term energy alternative planning system LEAP model based on the historical industry data corresponding to the multiple industry dimensions and the multiple groups of influencing factor indicators, including: Based on the historical industry data corresponding to the multiple industry dimensions and the multiple groups of influencing factor indicators, the LEAP model is used to predict the industry energy forecast results corresponding to the multiple industry dimensions under any energy forecast scenario, wherein the industry energy forecast results include one or more of the electricity demand forecast results, natural gas demand forecast results, and cooling / heating load demand forecast results corresponding to the industry; Summing the industrial energy forecast results corresponding to the multiple industrial dimensions to obtain an energy demand forecast result corresponding to any energy forecast scenario; The energy demand forecast results corresponding to each of the plurality of energy forecast scenarios are obtained by adopting a method of obtaining the energy demand forecast result corresponding to any one of the energy forecast scenarios.

3. The method according to claim 2, characterized in that The method of using the LEAP model to predict the industrial energy forecast results corresponding to the multiple industrial dimensions under any energy forecast scenario based on the historical industrial data corresponding to the multiple industrial dimensions and the multiple groups of influencing factor indicators includes: Based on the historical industrial data corresponding to the multiple industrial dimensions and the multiple groups of influencing factor indicators, the data analysis models corresponding to the multiple industrial dimensions built into the LEAP model are used to predict the industrial energy forecast results corresponding to the multiple industrial dimensions under any energy forecast scenario, wherein the multiple industrial dimensions include the agricultural production dimension, the transportation dimension, the tourism development dimension, and the residents' life dimension.

4. The method according to claim 3, characterized in that Using the data analysis model corresponding to the agricultural production dimension built into the LEAP model, the power demand forecast result corresponding to the agricultural production dimension is obtained in the following manner: in, represents the electricity demand forecast result of the agricultural production dimension in year t; is the electricity load in the tth year of agricultural product planting; is the electricity load of agricultural product processing in year t; The electricity load of the other parts of the agricultural production dimension in year t, where t is the preset forecast year; and The cooling / heating load demand forecast results corresponding to the agricultural production dimension are obtained as follows: in, represents the cooling / heating load demand forecast result of the agricultural production dimension in year t; is the cooling / heating demand of the warehousing link in year t, where The total warehouse area of ​​the target area in the pre-planned year t; is the growth coefficient of warehouse construction area in the target area; The cooling / heating demand per square meter of the warehouse; is the cooling / heating demand in year t for the other parts of the agricultural production dimension.

5. The method according to claim 3, characterized in that Using the data analysis model corresponding to the transportation dimension built into the LEAP model, the power demand forecast result corresponding to the transportation dimension is obtained in the following manner: in, represents the forecast result of the electric energy demand of the transportation dimension in year t, is the number of private cars in the target area in year t, is the number of buses in the target area in year t, is the average annual electricity demand of private cars, is the average annual electricity demand of buses, is the electricity load of other parts of the transportation dimension in year t.

6. The method according to claim 3, characterized in that The data analysis model corresponding to the tourism development dimension built into the LEAP model is used to obtain the power demand forecast result corresponding to the tourism development dimension in the following manner: in, represents the forecast result of electricity demand for the tourism development dimension in year t; is the electricity load in the tth year of the house construction phase; is the electricity load of the accommodation link in year t; The electricity load in year t for the other parts of the tourism development dimension; and The natural gas demand forecast results corresponding to the tourism development dimension are obtained in the following way: in, represents the natural gas demand forecast result of the tourism development dimension in year t; is the natural gas demand of the catering sector in year t; is the natural gas demand in year t for the other components of the tourism development dimension; and The cooling / heating load demand forecast results corresponding to the tourism development dimension are obtained by the following method: in, represents the cooling / heating load demand forecast result of the tourism development dimension in year t; is the cooling / heating demand of the accommodation link in year t, where The number of rural tourists in the study area in year t that is pre-planned for the target area; β tourist is the growth coefficient of the number of rural tourists in the target area; The average cooling / heating demand per rural tourist in the target area; is the cooling / heating demand of other parts of the agricultural production sector in year t.

7. The method according to claim 3, characterized in that The data analysis model corresponding to the resident life dimension built into the LEAP model is used to obtain the power demand forecast result corresponding to the resident life dimension in the following manner: in, represents the predicted result of the electricity demand in the t-th year of the residents’ living dimension, is the electric energy load for daily lighting in year t, are the electricity load of washing in year t, The electricity load in the other parts of the residential living dimension in year t; and The natural gas demand forecast results corresponding to the residents' living dimensions are obtained in the following way: in, represents the natural gas demand forecast result of the residents’ living dimension in year t, is the natural gas demand for cooking in year t, The natural gas demand in year t for the other parts of the residential living dimension; and The cooling / heating load demand forecast results corresponding to the residents' living dimensions are obtained by the following method: in, represents the cooling / heating load demand forecast result of the resident living dimension in year t, is the cooling / heating demand of the air conditioner in year t, is the cooling / heating demand of heating in year t; is the cooling / heating demand of other parts of the residents’ living dimensions in year t.

8. The method according to claim 3, characterized in that When the multiple industry dimensions also include an industrial and commercial dimension, the data analysis model corresponding to the industrial and commercial dimension built into the LEAP model is used to obtain the power demand forecast result corresponding to the industrial and commercial dimension in the following manner: in, is the electricity demand forecast result of the industrial and commercial dimension in year t, is the electric energy load of the smelting process in year t, is the electric energy load of the electrolysis link in year t, The electricity load in year t for the rest of the industrial and commercial dimension; and The natural gas demand forecast results corresponding to the industrial and commercial dimensions are obtained in the following way: in, is the natural gas demand forecast result for the industrial and commercial dimension in year t, is the natural gas demand of industrial and commercial users in year t; is the natural gas demand in year t for other parts of the industrial and commercial dimension; The cooling / heating load demand forecast results corresponding to the industrial and commercial dimensions are obtained in the following way: in, is the cooling / heating load demand forecast result of the industrial and commercial dimension in year t; is the cooling / heating demand of the accommodation link in year t, where is the total output of the target area in the pre-planned year t; tourist is the production capacity growth coefficient of the target area; The cooling / heating demand per unit capacity of the target area; is the cooling / heating demand in the process reduction phase in year t; is the cooling / heating demand in year t for the rest of the industrial and commercial dimension.

9. The method according to any one of claims 1 to 7, characterized in that After obtaining energy demand forecast results corresponding to the multiple energy forecast scenarios for the target area using the LEAP model of the long-term energy alternative planning system based on the historical industry data corresponding to the multiple industry dimensions and the multiple groups of influencing factor indicators, the method further includes: Based on the energy demand forecast results corresponding to the multiple energy forecast scenarios, the energy development strategies corresponding to the target area in the multiple energy forecast scenarios are determined.

10. The method according to claim 9, characterized in that In a case where the energy development strategy includes an energy structure adjustment strategy and an energy scheduling strategy, determining the energy development strategies corresponding to the multiple energy forecast scenarios for the target area based on the energy demand forecast results corresponding to the multiple energy forecast scenarios respectively includes: Comparing the differences between the electricity demand forecast results, natural gas demand forecast results, and cooling / heating load demand forecast results corresponding to any energy forecast scenario, and identifying the target energy demand and peak demand period under any energy forecast scenario, wherein the target energy is the main demand energy of the target area under the corresponding energy forecast scenario; Determining an energy structure adjustment strategy corresponding to any energy forecast scenario based on the target energy demand forecast result; Determining an energy scheduling strategy corresponding to any one of the energy forecast scenarios based on the peak demand period; The energy development strategies corresponding to any one of the energy forecast scenarios are obtained to obtain the energy development strategies corresponding to the multiple energy forecast scenarios.

11. An electronic device, characterized in that: It includes one or more processors and a memory, wherein the memory is used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the regional energy demand forecasting processing method described in any one of claims 1 to 10.

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