122 results about "Energy demand" patented technology

The invention relates to the technical field of feed efficiency optimization, in particular to a feed efficiency optimization system based on a growth model, and the system comprises a data collection unit which is used for collecting and associating individual identities, individual weight data, time-phased feed intake data, environment data and environment forecast data; the model calibration unit is used for calibrating individual energy conversion efficiency parameters, generating an environment dynamic correction factor by using the environment data and generating a group classification label based on historical individual performance data; and the decision and execution unit operates a double-cycle cooperation mechanism based on pre-judgment and execution. By dynamically calibrating the individual energy conversion efficiency and combining with the environment forecast, real-time calculation and differentiated feeding regulation and control of the individual energy demand can be realized.

The invention discloses a multi-element flexible load feature extraction method based on hydrogen energy demand response, and relates to the technical field of hydrogen energy demand resource planning. In order to construct an optimization control model responding to a power grid signal, hydrogen production efficiency maximization and power utilization planning rationalization are realized. The method specifically comprises the following steps: obtaining new energy power generation power characteristic data, predicting daily average generating capacity data according to the data, obtaining all-day time-of-use electricity price characteristic data of a commercial power grid, and dividing the data into peak / flat / valley time periods; according to the multi-element flexible load feature extraction method and the control strategy, the electric energy use strategy and the hydrogen production and hydrogen supply equipment operation strategy suitable for the whole hydrogen energy supply project can be formulated according to the stirring filtering of the new energy generating capacity, so that the energy consumption is reduced on the premise that the stable and sufficient hydrogen supply amount is ensured. The power consumption of the hydrogen energy supply project in the peak period and the flat period of the power grid is effectively reduced, and the comprehensive energy efficiency and the green power consumption rate of the hydrogen energy supply project are effectively improved.

The invention relates to the technical field of industrial intelligent control, in particular to an electric discharge machine intelligent scheduling and production regulation and control system which comprises a staggered analysis module, a station mapping module, a task distribution module, a trend prediction module and an instruction regulation and control module. According to the method, the processing task parameters and the station bearing parameters of different dimensions are standardized and uniformly mapped, and then the spatial distance is calculated to accurately quantify the matching degree between the tasks and the stations, so that the limitation of static distribution can be eliminated, and redundant production resources are identified and eliminated by analyzing the change of the matching degree; according to the energy demand and time urgency dynamic remapping of a task, simplification and efficiency improvement of production configuration are achieved, meanwhile, the future abrasion risk can be foreseen by collecting key component contour data in real time and converting the key component contour data into a quantitative integrity index, establishing a state dynamic change track and conducting trend extrapolation, and the method has the advantages of being high in practicability and the like. And finally, carrying out comprehensive decision making according to the prediction result and the real-time load margin.

The invention relates to an energy storage optimization configuration method based on a green power direct connection project. The method comprises the following steps: step 1, determining an optimization configuration limiting condition and a price mechanism, wherein the core of source, load and storage configuration is a green power direct connection system so as to meet the green energy demand of an enterprise and improve the nearby local consumption level of new energy; 2, green power direct connection project planning configuration: exploring an optimal scheme under a differentiated scene by applying a production operation simulation method and taking system economy optimization as a target, analyzing the influence of a source storage configuration scale on project economy, and considering surrounding actual resource conditions to obtain a final feasible scheme; 3, analyzing a green electricity direct connection project planning scheme based on examples; the method has the advantages that the new energy utilization rate is increased, the cost is reduced, and reasonable distribution and storage are realized based on a production simulation method.

The multi-mode ultra-low energy consumption building kitchen makes-up air exhaust air coupling multi-stage heat recovery system relates to the kitchen ventilation technical field.The present application solves the problems of high operation cost and maintenance workload of the existing kitchen heat recovery system, increases the complexity and installation difficulty of the system, and affects the heat recovery efficiency of the system. In the normal working mode, the make-up air and exhaust air system runs simultaneously, fresh air enters the kitchen through the make-up air channel, and waste air is discharged through the exhaust air channel. When the temperature in the exhaust air exceeds the set threshold, the control system will trigger the heat energy recovery mode. In this mode, the excess heat energy will be introduced into the heat energy storage tank to store the excess heat. During the energy demand peak period, the control system will start the energy supply mode. At this time, the heat energy in the heat energy storage tank can be released to supply the additional heat energy demand. The present application realizes heat energy recovery and efficient use of energy by coupling the kitchen make-up air and exhaust air system, heat exchange and energy storage device.

The invention provides a Java-based diesel generator intelligent start-stop and power dynamic distribution dynamic adaptive method, and belongs to the technical field of hybrid energy management, and the method comprises the steps: S1, importing photovoltaic data and load data, and processing the photovoltaic data and the load data to the same granularity; s2, establishing a dynamic power gap calculation model, and providing an accurate data basis for diesel generator intervention based on photovoltaic and load prediction data; s3, starting a time window fusion intelligent decision algorithm, analyzing the time sequence characteristics of a power gap, replacing multiple times of short-time start and stop with single-time long-time operation, and improving the load rate of each time of operation; and S4, deriving a result through a start-stop time reverse derivation mechanism based on the total energy, firstly determining the moment when the diesel engine needs to stop power supply, and then reversely calculating the latest start moment according to the total energy gap and the rated power of the diesel engine in the window, thereby ensuring that the operation duration of the diesel engine is accurately matched with the actual energy demand, avoiding the waste of fuel oil, and reducing the energy consumption of the diesel engine. And high efficiency and economical efficiency of the operation process are realized.

A federated learning framework including household agents configured to continuously learn model parameters for managing charging periods and discharging periods of household batteries, and microgrid agents to maximize use of local energy based on a pricing policy, including accessing power from other microgrids when there is insufficient local energy to cover local demand. and selling surplus energy to the other microgrids when power generation by the microgrid surpasses the local demand. Each household machine learning agent is configured to control household energy demand from and supply to a microgrid which they are connected in order to minimize household energy cost while adapting to changes in the energy price that is determined based on the pricing policy of the microgrid agent that encourages reduction of carbon emission. A federated learning engine combines the model parameters from the household machine learning agents to update a global household machine learning agent.

Described is a process for the recycling of polycarbonate from particulate polycarbonate waste comprising a) providing the particulate polycarbonate waste, b) solvent-based purification of the particulate polycarbonate waste, c) partially depolymerizing of the purified polycarbonate, d) repolymerizing of the partially depolymerized polycarbonate. The process of the invention is an improved and adjustable process for the recycling of polycarbonates in high quality and purity, which is applicable on a technical and commercial scale at a relatively low energy demand.

This application relates to the field of power grid optimization dispatching technology, specifically to a multi-objective optimization dispatching method and system for active distribution networks with source-grid-load-storage coordination. The method includes: collecting the energy storage capacity of each renewable energy power station in the active distribution network, and the energy dispatched by each transformer from each renewable energy power station; calculating the priority dispatching characteristics of each renewable energy power station; predicting the future energy demand of each transformer and the future energy storage capacity of the renewable energy power stations supplying them; coordinating the transmission distances between each transformer and the renewable energy power stations supplying them; determining the dispatch matching coefficient between the renewable energy power stations and transformers; based on the dispatch matching coefficient, constructing an objective function with the goal of minimizing the source-grid-load-storage coordinated dispatching cost and maximizing renewable energy consumption; and using a multi-objective optimization algorithm to solve for the optimal solution, which is then used for power dispatching. This improves the rationality of power resource dispatching in the distribution network.

The invention discloses a whole-house intelligent energy storage system management method based on energy demand prediction, and relates to the technical field of energy management, and the method comprises the following steps: 1, carrying out the statistics of the energy demand of a whole house, carrying out the statistics of electric equipment and power consumption in the statistics process, and carrying out the centralized collection of the data after statistics; 2, after the collection of the data of the electric equipment and the power consumption is completed, the power consumption data of the electric equipment is counted in real time, and the electric equipment is divided after the statistics is completed; and 3, intelligent optimization regulation and control processing is carried out, a real-time regulation and control prediction scheme is formulated for real-time prediction, and an energy-saving control scheme is formulated for regulation and control. In the intelligent management control process of the whole house, the real-time statistical analysis result is more accurate by classifying the electric equipment and counting the electricity consumption in detail, and meanwhile, the collected frequency and error threshold are controlled during real-time counting, so that the implementation efficiency of a regulation and control prediction scheme is higher.

A kind of hydrate method cold accumulation and cooling system based on LNG cold energy, including gas supply system, hydrate method cold accumulation system, cooling system and refrigerating unit auxiliary system.The huge cold energy released in LNG gasification is used in gas supply system by deionized water and resident building return water coolant water in turn.The absorbed LNG cold energy is accumulated in hydrate method cold accumulation system, and the cold accumulation system can separate hydrate and deionized water, and can realize variable rate cold accumulation.The coolant water in cooling system exchanges heat with LNG and hydrate slurry in turn, and is sent to cold storage and resident building user by circulating pump pressurization, when the cooled coolant water cannot meet the requirement of cold energy demand end or cooling system cannot stably cool, refrigerating unit auxiliary system is opened, the opening of distribution valve is adjusted according to the temperature of cold energy demand end, the coolant water through distribution valve and the coolant water without distribution valve are mixed in mixer, to meet the cold energy demand of demand end.The system has the advantages of energy saving, sustainable and stable cooling, and has the value of commercial application.

The embodiment of the specification discloses a kind of electric-hydrogen-thermal energy system construction methods of multi-type energy demand scene.The method includes by constructing the system simulation model corresponding to electric-hydrogen-thermal energy system, then based on target cascade analysis method determines the target limit condition corresponding to system simulation model under target energy demand scene, finally according to annual total construction resource function determines the system target equipment parameter of electric-hydrogen-thermal energy system under target limit condition, and according to system target equipment parameter constructs target electric-hydrogen-thermal energy system.Through the construction method of above target electric-hydrogen-thermal energy system, it meets the multi-type energy demand of different scenes, while, the optimal system equipment parameter is determined automatically, reduces system construction cost, also by introducing heat storage tank realizes the heat recovery of hydrogen fuel cell, improves the renewable energy utilization efficiency of system, improves the use satisfaction of user.

The present disclosure provides an advanced energy management system that optimizes energy storage unit (ESU) deployment and operation using artificial intelligence. Unlike conventional systems that rely on static configurations or manual adjustments, this system dynamically analyzes data from multiple sources to configure and manage ESUs. By employing AI algorithms, the system can adapt to specific application requirements, predict future energy needs, and efficiently manage energy flow across distributed storage units. The system generates and evaluates multiple ESU configurations, selecting the optimal setup based on performance metrics and application-specific parameters. This approach addresses the limitations of traditional ESU deployment methods, potentially improving overall energy efficiency, enhancing reliability, and reducing costs in various applications, from small-scale residential installations to large industrial and grid-level energy storage solutions. The AI-driven system continuously monitors and optimizes ESU performance, adapting to changing conditions and maintaining ultra-high reliability standards.

The invention relates to a smart energy management system and method based on big data analysis, and the system comprises an energy demand prediction module which is used for inputting the historical energy demand quantity into an energy demand prediction model, so as to determine the energy demand prediction quantity of a to-be-managed building site; the energy risk prediction module is used for calling the energy risk assessment model to determine an energy risk value of the to-be-managed building site according to the historical energy risk site and the energy risk frequency; the energy distribution control module is used for constructing an energy distribution control model corresponding to the target area according to the energy demand predicted quantity and the energy risk value corresponding to each to-be-managed building site, and solving the energy distribution control model by adopting a multi-target optimization algorithm so as to determine the energy distribution quantity of each to-be-managed building site; and the energy management module is used for inputting the energy distribution amount to the corresponding to-be-managed building site. According to the invention, the energy utilization efficiency is improved, and the safety redundancy capability of the energy management system is enhanced.

This invention relates to the field of security technology, specifically to a control module and method for a comprehensive energy security system. It includes an analysis and prediction unit, an evaluation and calculation unit, a variable consumption unit, and a control strategy unit. The demand prediction module of this invention receives user periodic electricity consumption, temperature deviation, and precipitation deviation from the analysis and analysis module. It calculates a baseline energy demand based on the user periodic electricity consumption, and then calculates the impact of temperature deviation on user energy demand under extreme weather conditions. By combining the baseline energy demand and the impact of extreme weather conditions, it predicts the user's energy demand under extreme weather conditions. This combination allows for more accurate prediction of energy demand under extreme weather conditions, enabling advance adjustments to power generation plans and power dispatch, avoiding energy waste caused by supply-demand imbalances or excessive microgrid generation, optimizing resource allocation, and improving resource utilization efficiency.

The invention relates to the technical field of agricultural and pastoral integrated energy systems, in particular to an electricity-carbon coupling price-driven agricultural and pastoral integrated energy system operation scheduling method, which comprises the steps of S1, establishing an energy supply and demand characteristic model and an electricity-carbon transaction market mechanism of an agricultural and pastoral integrated energy system; s2, constructing an electricity-carbon coupling price model; s3, by taking the maximum operation income of the operator side and the maximum efficiency income of the user side of the agricultural and pastoral park as targets, constructing an electricity-carbon coupling price-driven agricultural and pastoral park comprehensive energy system double-layer optimization decision model; s4, solving the double-layer optimization decision model to obtain a floating carbon price coefficient and a user energy consumption demand; s5, substituting the floating carbon price coefficient into an electricity-carbon coupling price model to calculate an electricity-carbon coupling price; and S6, guiding the operation scheduling of the comprehensive energy system of the agricultural and pastoral park based on the electricity-carbon coupling price and the energy demand of the user. According to the invention, the practicability and comprehensiveness of the operation decision of the comprehensive energy system in the agricultural and pastoral park in the electricity-carbon coupled market can be improved.

The invention provides a new energy automobile energy station scheduling method and system considering source load uncertainty, and the energy station integrates a photovoltaic power generation device, an electrolytic hydrogen production device, a hydrogen fuel cell, a hydrogen storage device, a battery replacement device and a charging device. The method comprises the following steps: constructing respective operation models of a photovoltaic power generation device, an electrolytic hydrogen production device, a hydrogen fuel cell and a hydrogen storage device, representing predicted values of photovoltaic power generation power, power conversion power, charging power and hydrogenation quantity as fuzzy variables by using fuzzy opportunity constraints, and establishing a multi-time-scale scheduling model comprising day-ahead optimization and intra-day rolling optimization. The scheduling scheme is obtained through clear equivalence conversion and model solution, so that collaborative optimization of the power and hydrogen coupling system can be realized, dual uncertainty of photovoltaic power generation and energy consumption requirements of new energy vehicles is effectively dealt with, the utilization rate of clean energy is improved, the power purchase cost of a power grid is reduced, and the economical efficiency and reliability of the scheduling scheme are improved.

The invention discloses an energy room environment intelligent control method and system based on renewable energy fluctuation, and relates to the technical field of building energy intelligent management, and the method comprises the steps: collecting the real-time operation data of an energy room, initializing the environment memory and the energy memory, and storing the environment memory and the energy memory; combining external weather prediction and a thermodynamic model to generate an energy demand prediction quantity under a comfortable condition; comparing the energy demand predicted quantity with the real-time power, and calculating to obtain a cheap energy index representing the renewable energy remaining degree; a virtual price curve is constructed by combining environment memory and energy memory, and channel weight distribution of an execution layer is generated; under the constraint of the virtual price curve and the channel weight, a joint optimization problem is established in combination with a thermodynamic model, a control instruction is solved and obtained, execution feedback is written back to environment memory and energy memory, and a self-adaptive closed loop is formed; the indoor comfort and the energy economy can be kept under the fluctuation condition, and the utilization rate of renewable energy sources and the stability of system operation are improved.

A method and system for real-time carbon emission control based on multi-source data fusion and AI decision-making is disclosed. This method relates to a data processing system or approach specifically applicable to administrative, commercial, financial, management, supervisory, or predictive purposes. Real-time production data is collected via IoT devices and combined with a carbon accounting engine to calculate real-time carbon emission intensity. An AI optimization layer is constructed, incorporating a process knowledge graph, a long short-term memory network, and a reinforcement learning agent. This layer transforms process constraints into mathematical boundary conditions, predicts energy demand, and iteratively optimizes the process with the goal of minimizing carbon emission intensity, generating a dynamic scheduling strategy to achieve real-time adjustment of production equipment operating parameters. This application aims to improve the accuracy of carbon emission monitoring data in production scheduling and reduce the risk of high-carbon-intensity production due to inappropriate time selection by enterprises.

The present application provides an optimization scheduling method considering the participation of chicken house flexible resources in power distribution network peak load shifting, and relates to the cross technical field of facility agriculture and power grid optimization scheduling, which comprises the following steps: based on the industry load in the whole cycle, using the heat production equation and mechanism model equation of cage chicken house application scene, a typical cage chicken house daily constant temperature energy demand model is constructed; based on the constructed typical cage chicken house daily constant temperature energy demand model, the cage chicken house load is classified, and the classified load is superimposed to construct a load flexibility potential model; based on the constructed load flexibility potential model and the power grid peak load shifting model of adjustable load, by setting the objective function and constraint condition, and using the model optimization algorithm, the optimization scheduling scheme of power distribution network peak load shifting is obtained. The power grid peak load shifting model based on adjustable load constructed by the present application smooths the load curve of power grid, and realizes the economic operation of power grid.

The invention discloses a rural comprehensive energy system optimal configuration method based on multi-energy complementation. The method comprises the following steps: S1, collecting multi-dimensional rural energy data; s2, mathematical modeling of the energy equipment; s3, performing matching analysis on the rural multi-energy demand and the equipment; s4, constructing a multi-objective optimization constraint system; s5, constructing and solving an optimal configuration model of the integrated energy system; according to the method, layered modeling is carried out on the performance parameters of the energy conversion type equipment and the cold and heat supply type equipment, so that the suitability and accuracy of rural comprehensive energy system equipment modeling are effectively improved; a multi-agent system is adopted to carry out matching analysis of rural multi-energy demands and equipment, and it is ensured that multi-energy complementary potential evaluation can accurately adapt to multi-element demands of different rural scenes; an optimal configuration model is solved by constructing a target function fused with a rural power grid vulnerability index, so that an optimal configuration result can realize collaborative optimization and can adapt to the characteristics of a rural power grid.

The invention discloses a new energy geothermal power generation cooperative operation method, and relates to the technical field of new energy, and the method comprises the steps: judging whether a to-be-measured distance between target new energy and target geothermal energy is smaller than a preset distance division interval value; if the to-be-measured distance is between the distance division interval values, a target transmission line between the target new energy and the target terrestrial heat is divided into a plurality of equal sections to obtain a first section line, if the to-be-measured distance is between the distance division interval values, the target transmission line is divided into sections which are three times of the first section line, and if the to-be-measured distance is greater than the distance division interval values, the target transmission line is divided into sections; if yes, performing section division of a six-time first section line on the target transmission line, obtaining a temperature loss correlation coefficient according to correlation influence change trend characteristics between historical environment temperature data and historical extra energy loss degree values in historical monitoring data, and performing error compensation on the initially set energy consumption predicted value according to the temperature loss correlation coefficient to obtain an initial energy consumption predicted value; the actual energy demand quantity is obtained, and power generation cooperative operation is conducted. The power generation efficiency can be improved.

The invention discloses a factory integrated energy system optimization operation method considering a production network and an energy network. The method comprises the following steps: establishing a carbon emission knowledge graph of each link of the production network and a carbon emission knowledge graph of each device of the energy network through an energy-saving and carbon-reducing intelligent agent fused with a large language model, generating an energy-saving and carbon-reducing diagnosis report, and formulating an energy-saving and carbon-reducing reconstruction scheme; in a current system operation stage, carrying out analogue simulation on the optimal energy-saving and carbon-reducing reconstruction scheme, and respectively establishing energy prediction models of a production network and an energy network; the method comprises the following steps of: sensing energy consumption predicted values, carbon emission intensity predicted values and carbon emission predicted values of a production network and an energy network in each time period in the future through a decision-making agent, and judging whether a high-carbon low-efficiency link / equipment exists or not through an energy demand and a production scheduling plan of the production network in each time period in the future and an energy supply plan and a global carbon emission index of the energy network in each time period in the future; if yes, a system strategy adjustment mechanism is triggered, and a low-carbon economic optimization operation model of the dynamic alliance of the production network and the energy network is established.

This invention relates to the field of carbon emission forecasting technology, providing a carbon emission forecasting method, system, computer equipment, and storage medium. The method includes: decomposing the industrial-specific final energy consumption data from the overall industry-specific final energy consumption data based on historical annual industrial end-product energy consumption data by category and type; predicting and analyzing this data to obtain the target year's overall industry-specific final energy consumption forecast data; and calculating the primary energy consumption forecast data based on the target year's overall industry-specific final energy consumption forecast data using power generation ratio prediction data obtained from historical annual electricity data evolution analysis, thereby obtaining the predicted value of regional energy consumption carbon emissions. This invention can capture the natural laws governing the impact of energy structure adjustment, technology diffusion, and resource constraints on energy demand and carbon emissions, improving the reliability and accuracy of regional energy carbon emission forecasting.

This invention discloses a system and method for hydrogen production from waste aluminum and resource utilization of byproducts, belonging to the field of solid waste treatment and resource utilization technology. This invention innovatively integrates four technologies—hydrogen production from waste aluminum alkaline solution, calcification for silicon removal, seed decomposition for aluminum extraction, and intelligent alkaline solution recycling—into a continuous system. While producing hydrogen from waste aluminum, it simultaneously recovers aluminum hydroxide and calcium silicate resources, improving resource utilization. A staged impurity removal strategy is adopted to ensure the purity of byproducts while simultaneously achieving the recycling of alkaline solution, thereby reducing process costs and improving environmental benefits. An energy recovery and utilization unit is set up to use the heat released from the tail gas and hydrogen production reaction to heat the internal heating devices, significantly reducing external energy demand. An inert gas recovery device is also equipped in the hydrogen storage unit to reduce external material consumption, thus improving the economics and engineering feasibility of the process.

The airflow power plant and the energy fund storage system provide a sustainable, low-maintenance and scalable solution for continuous and trouble-free energy generation. While the airflow power plant continuously ensures sustainable production of energy, the surplus energy generated is stored in innovative, low-maintenance and location-independent energy fund storage systems when demand is lower. These systems, which can be implemented in a space-saving manner in a vertical or inclined structure, ensure that the stored energy is immediately available when the demand for energy increases. The use of natural pressure differences and the use of gravitational forces make it possible to flexibly adapt the system to different locations and energy demands, as a result of which both small and large infrastructures can be developed. This solution maximises energy efficiency and provides a versatile, adaptable storage option for renewable energies. The functionality is based on the use of natural environmental conditions, such as air pressure differences between valley and peak positions, temperature differences as a result of the altitude, additional heat sources as required, storage by gravitational force and flexible air pressure and temperature control. With these elements, the system adapts flexibly to different locations and energy demands, as a result of which it is suitable both for small and for large-area applications. This maximises energy efficiency and provides a versatile, adaptable storage option for renewable energies.

A natural gas zero-carbon heating and carbon material co-production system based on liquid metal catalytic cracking and a control method thereof. The present application belongs to the technical field of energy chemical industry and clean heating. The present application solves the problems of low utilization rate, high carbon emission and poor economy of traditional heating equipment. The present application uses pipeline natural gas as raw material, which is cracked into hydrogen and solid carbon in a high-temperature liquid metal catalytic cracking reactor; the hydrogen is fed into a double-loop combustion chamber, a part of which is combusted to provide zero-carbon heating for the terminal, and the other part of which is combusted to provide heat energy back to the cracking reactor to form an energy closed loop; the solid carbon is separated and post-processed into high-value carbon material products. The present application is provided with a dynamic carbon management device and an arc-shaped flow guide inner wall to ensure long-term continuous operation of the reactor, and can also realize heat, electricity and cold combined supply through valve switching. The present application realizes the collaborative conversion of natural gas into clean heat energy and high-value carbon materials, has the characteristics of zero-carbon environmental protection, severe cold adaptation and economic profitability, and meets the demand for composite energy.

The application discloses an energy data management method, system and device of a movable intelligent building cabin, and relates to the technical field of data management. The method comprises the following steps: deploying a sensor network on the movable intelligent building cabin, collecting energy data, environmental data and user behavior data in real time, and performing correlation fusion to generate an energy real-time state vector; constructing an energy output prediction model and an energy demand prediction model; performing energy prediction on the energy real-time state vector in a preset time window to obtain energy output prediction parameters and energy demand prediction parameters; performing energy regulation analysis on the energy output prediction parameters and the energy demand prediction parameters to determine target energy regulation strategy parameters, and performing dynamic management on energy data. The technical problems that the prior art is difficult to dynamically adapt to changes in the energy demand of the movable building cabin, resulting in energy waste and supply-demand mismatch are solved, the technical effect of intelligently regulating the energy management strategy and improving the energy supply-demand matching degree is achieved.

The present disclosure provides a computer-implemented method of modulating energy consumption by a water provision system installed in a building, the water provision system comprising a heat pump configured to transfer thermal energy from outside the building to a thermal energy storage medium inside the building and a control module configured to control operation of the water provision system, the water provision system being configured to provide water heated by the thermal energy storage medium to one or more water outlets and further configured to supply heated water to a central heating system configured to raise an indoor temperature of the building, the method being performed by the control module and comprising: determining a level of energy demands of a geographical region comprising the building; and upon determining that the level of energy demands is low, operating the heat pump to store thermal energy in the thermal energy storage medium, and operating the water provision system to supply heated water to the central heating system using thermal energy stored in the thermal energy storage medium.

The invention discloses a rule-driven rural multi-energy complementary distributed energy technology coupling optimization method, which comprises the following steps of performing quantitative evaluation on rural localized primary energy endowment and terminal multi-element energy demand characteristics, and generating standardized resource endowment data and energy demand data; constructing a rule engine based on a forward chain reasoning generative system and a hierarchical rule, inputting resource endowment data and energy consumption demand data into the rule engine, and generating a multi-energy complementary preliminary technology coupling scheme; and verifying the feasibility of the preliminary technical coupling scheme, and outputting a final technical coupling scheme and an analysis result. The method has the beneficial effects that in a rural energy system planning stage, based on rural polymorphic resource endowment and energy consumption requirements, a coupling suggestion scheme of a multi-category distributed energy technology is output through an intelligent rule engine, automatic generation of a technology coupling scheme is realized, the planning efficiency is remarkably improved, and carbon emission and energy consumption cost are reduced.