Building power utilization intelligent regulation and control system based on renewable energy power generation

Abstract

The invention belongs to the technical field of electric power pre-distribution, and discloses a building power utilization intelligent regulation and control system based on renewable energy power generation, and the system comprises a power generation module which is disposed on the outer surface of a building, is used for converting the collected renewable energy into electric energy, supplies power to a power utilization device in the building, and is used for predicting a power generation result in a preset time period; the energy storage module is connected with the power generation module and is used for storing the electric energy converted by the power generation module and supplying power to a power utilization device under a preset condition; and the power distribution module is used for planning a power distribution scheme according to the power generation result and controlling start and stop of the power utilization device and the power utilization quota according to the power distribution scheme. According to the invention, power supply of photovoltaic power generation, the energy storage module and the commercial power can be intelligently allocated, and high efficiency and stability of building power utilization are ensured. The operation state of the electric appliance is accurately controlled through the power distribution module, the electric quantity obtained by photovoltaic power generation can be utilized to the maximum extent, the dependence on the traditional mains supply is effectively reduced, and thus the building energy consumption is greatly reduced.

Description

Technical Field

[0001] The invention discloses an intelligent control system for building electricity based on renewable energy power generation, belonging to the technical field of power pre-distribution. Background Art

[0002] Ultra-low energy buildings are buildings designed to adapt to specific climate characteristics and natural conditions. These buildings use enclosure structures with significantly improved thermal insulation and airtightness, combined with fresh air heat recovery technology, and effectively use renewable energy to provide users with an energy-saving and comfortable indoor environment. In terms of design, ultra-low energy buildings mostly adopt passive design strategies, which include choosing appropriate building orientations, using heat storage materials, setting up shading devices, and promoting natural ventilation, aiming to minimize reliance on active energy systems.

[0003] To further reduce energy consumption, ultra-low energy buildings are usually covered with photovoltaic panels to supply the electricity needed for daily use. However, photovoltaic power generation has an inherent challenge in that its power generation efficiency is significantly affected by weather conditions, resulting in unstable power output. Therefore, traditional power distribution methods are not applicable to ultra-low energy buildings because it is impossible to accurately predict the power supply of photovoltaic power generation. At present, this field still faces an urgent problem to be solved: how to develop an intelligent power management system for such buildings that can adapt to the instability of photovoltaic power generation and ensure the reasonable distribution of power supply. Summary of the invention

[0004] The purpose of this application is to provide an intelligent control system for building electricity based on renewable energy power generation, so as to solve the problem that the power generation efficiency of renewable energy power generation in the prior art is significantly affected by weather conditions, resulting in unstable power output. The traditional power distribution method is limited in application in ultra-low energy consumption buildings because it cannot accurately predict the power supply of renewable energy power generation. To achieve the above purpose, the present invention proposes an intelligent control system for building electricity based on renewable energy power generation, including: a power generation module, an energy storage module and a power distribution module;

[0005] The power generation module is arranged on the outer surface of the building, and is used to convert the collected renewable energy into electrical energy, supply power to the electrical devices in the building, and to predict the power generation results within a preset time period;

[0006] The energy storage module is connected to the power generation module, and is used to store the electric energy converted by the power generation module and supply power to the electric device under preset conditions;

[0007] The power distribution module is used to plan a power distribution plan according to the power generation result, and control the start and stop and power consumption quota of the power-consuming device according to the power distribution plan.

[0008] Preferably, the power distribution module comprises a distribution unit and a control unit;

[0009] The distribution unit is used to plan a power distribution plan according to the power generation results and the operating characteristics of the electrical devices in the building, and determine the power distribution parameters according to the power distribution plan;

[0010] The control unit is used to control the start and stop and power consumption quota of the electrical devices in the building according to the power distribution parameters.

[0011] Preferably, the allocation unit includes a matching subunit and a parameter determination subunit;

[0012] The matching subunit is used to match the operating characteristics of the power-consuming device and the power generation result predicted by the power generation module based on a genetic algorithm;

[0013] The parameter determination subunit is used to plan a power distribution plan according to the matching result, and determine the electric energy distribution parameters according to the power distribution plan.

[0014] Preferably, it also includes an optimization subunit;

[0015] The optimization subunit is used to optimize the genetic algorithm, and perform iterative matching calculations according to the optimized genetic algorithm to select matching results that meet preset requirements.

[0016] Preferably, the power generation module includes a power generation prediction unit and a power generation unit;

[0017] The power generation prediction unit is used to predict the power generation at different times within a preset period according to the weather parameters of the area to which the building belongs, and record the power generation at different times within the preset period as the power generation result;

[0018] The power generation unit is used to convert the collected renewable energy into electrical energy.

[0019] Preferably, the power generation prediction unit includes a data acquisition subunit, a prediction subunit and a fusion analysis subunit;

[0020] The data acquisition subunit is used to collect weather parameters of the area to which the building belongs;

[0021] The prediction subunit is used to predict the power generation influencing factors at different times in a future preset period of time according to the weather parameters to obtain a prediction result;

[0022] The fusion analysis subunit is used to perform fusion analysis on the prediction results according to the multimodal neural network to obtain the power generation at different times within a preset time period.

[0023] Preferably, the data acquisition subunit includes an image acquisition unit and an image analysis unit;

[0024] The image acquisition unit is used to collect radar satellite images of clouds and atmospheric motion;

[0025] The image analysis unit is used to perform meteorological simulation on the radar satellite image based on a meteorological model to obtain weather parameters of the area to which the building belongs.

[0026] Preferably, the power generation unit specifically utilizes the photovoltaic effect to convert solar energy into electrical energy.

[0027] Preferably, the power generation module further comprises a cleaning unit;

[0028] The cleaning unit is used to clean the panel of the power generation unit.

[0029] Preferably, the energy storage module includes an alarm device and a plurality of energy storage devices;

[0030] The alarm device is used to issue an alarm when the amount of power stored in the multiple energy storage devices is lower than a preset value;

[0031] The multiple energy storage devices are used to supply power to the electrical devices in sequence using an automatic switching mechanism.

[0032] Compared with the prior art, the beneficial effects of the present invention are as follows: the intelligent control system for building electricity based on renewable energy power generation described in the present invention can intelligently allocate the power supply of photovoltaic power generation, energy storage modules and mains electricity to ensure the high efficiency and stability of building electricity. By accurately controlling the operating status of electrical appliances through the power distribution module, the power obtained from photovoltaic power generation can be maximized, effectively reducing the dependence on traditional mains electricity, thereby achieving a significant reduction in building energy consumption. The present invention not only promotes the widespread application of renewable energy, but also provides strong technical support for building a low-carbon, environmentally friendly and sustainable building electricity environment, which is of great significance for promoting the development of green buildings. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 Schematic diagram of the structure of the intelligent control system for building electricity according to an embodiment of the present invention. DETAILED DESCRIPTION

[0034] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments 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 creative work are within the scope of protection of the present invention.

[0035] See also Figure 1As shown, the purpose of this embodiment is to provide a building electricity intelligent control system based on renewable energy power generation, including: a power generation module, an energy storage module and a power distribution module;

[0036] The power generation module is arranged on the outer surface of the building, and is used to convert the collected renewable energy into electrical energy, supply power to the electrical devices in the building, and predict the power generation results within a preset time period; the energy storage module is connected to the power generation module, and is used to store the electrical energy converted by the power generation module, and supply power to the electrical devices under preset conditions; the power distribution module is used to plan the power distribution plan according to the power generation results, and control the start and stop and power quota of the electrical devices according to the power distribution plan.

[0037] The renewable resources of the present invention cover a wide range, including wind energy, solar energy and other clean energy. In order to more specifically explain the technical solution of the present invention, this embodiment will be explained with solar energy as the core resource. It is worth noting that the type of building used in the present invention is an ultra-low energy consumption building, which is known for its excellent energy efficiency and low environmental impact. The intelligent power control system of the present invention is tailor-made for this type of building, aiming to further improve its energy efficiency and environmental performance.

[0038] Among them, the start and stop of electrical devices and the power quota specifically refer to the flexible adjustment of the switch status and operating power of various electrical devices inside the building according to actual needs. For example, in ultra-low energy consumption buildings, since the building itself has excellent thermal insulation performance, the impact of shutting down the air-conditioning system for a short period of time on the indoor ambient temperature is relatively limited; at the same time, display devices such as televisions can automatically adjust the screen brightness according to the brightness of the surrounding environment to achieve energy saving effects. Through these intelligent control methods, the present invention aims to maximize the utilization of energy and minimize energy consumption.

[0039] Furthermore, the power generation module includes a power generation prediction unit and a power generation unit; the power generation prediction unit is used to predict the power generation at different times within a preset period of time according to weather parameters of the area to which the building belongs, and record the power generation at different times within the preset period of time as the power generation result; the power generation unit is used to convert the collected renewable energy into electrical energy.

[0040] Specifically, in this embodiment, the power generation module includes a power generation prediction unit and a power generation unit. The power generation prediction unit is a highly intelligent system that can accurately predict the power generation at various time points in a preset period of time in the future (such as one day, one week or even one month) based on the real-time weather parameters of the area where the building is located (covering key factors such as sunshine intensity, cloud thickness, temperature, etc.). These forecast data are recorded in detail and summarized as power generation results, providing a key basis for subsequent power distribution. The power generation unit focuses on efficiently converting the collected renewable energy (specifically solar energy in this case) into electrical energy for use by various electrical devices inside the building.

[0041] Furthermore, the power generation prediction unit includes a data acquisition subunit, a prediction subunit and a fusion analysis subunit; the data acquisition subunit is used to collect weather parameters of the area to which the building belongs; the prediction subunit is used to predict the power generation influencing factors at different times within a preset time period in the future according to the weather parameters, and obtain the prediction results; the fusion analysis subunit is used to perform fusion analysis on the prediction results according to a multimodal neural network, and obtain the power generation at different times within the preset time period.

[0042] Specifically, in this embodiment, the power generation prediction unit includes a data acquisition subunit, a prediction subunit and a fusion analysis subunit to achieve accurate prediction of future power generation; first, the data acquisition subunit is responsible for comprehensively collecting weather parameters in the area where the building is located, including but not limited to sunshine intensity, cloud thickness, temperature and wind speed, etc., which have a direct impact on the efficiency of solar power generation. Through high-precision sensors and real-time meteorological data interfaces, the data acquisition subunit can ensure that the latest and most accurate weather information is obtained.

[0043] Next, the prediction subunit uses the prediction algorithm model based on the collected weather parameters to accurately predict the factors affecting power generation at different time points in the future preset period (such as one day, one week or one month). These factors may include changes in solar radiation, cloud cover, temperature changes, etc., which will directly affect the power generation efficiency of solar panels. The prediction subunit outputs the prediction results in the form of data to provide a basis for subsequent analysis.

[0044] In order to further improve the accuracy of the forecast, the fusion analysis subunit uses a multimodal neural network to perform fusion analysis on the forecast results. This neural network can comprehensively consider the complex relationship between multiple weather parameters and factors affecting power generation, and intelligently process the forecast data through a deep learning algorithm to obtain more accurate and reliable power generation forecast results. These forecast results will serve as an important basis for the power distribution module to formulate a distribution plan.

[0045] Specifically, in this embodiment, the collection subunit collects light intensity and ambient temperature, and calculates the light intensity through a calculation submodule located in the collection subunit. The calculation formula of the light intensity is as follows:

[0046]

[0047] In the formula represents the light intensity in optics, represents the frequency of light, is the irradiated area, For time interval Inside The total number of photons on is Planck's constant.

[0048] It should be understood that, in another embodiment, the data acquisition subunit is used to obtain the weather forecast of the area to which the building belongs, and to collect weather parameters of the area to which the building belongs according to the weather forecast.

[0049] In order to ensure that the weather parameters collected by the data collection subunit are more accurate, in this embodiment, a collection module is used to collect radar satellite images of clouds and atmosphere by itself to predict weather information at different times in a preset period of time in the future.

[0050] Furthermore, the data acquisition subunit includes an image acquisition unit and an image analysis unit; the image acquisition unit is used to collect radar satellite images of clouds and atmospheric movements; the image analysis unit is used to perform meteorological simulation on the radar satellite images based on a meteorological model to obtain weather parameters of the area to which the building belongs.

[0051] In a specific embodiment of the present invention, the data acquisition subunit integrates two functional modules, image acquisition and image analysis, to achieve accurate capture and in-depth analysis of cloud dynamics and atmospheric motion information.

[0052] First, image acquisition units are deployed to receive and record high-resolution image data from radar satellites. These images focus on capturing cloud distribution, morphological changes, and other key motion features in the atmosphere. Through advanced remote sensing technology and data processing algorithms, image acquisition units are able to capture and store these complex weather phenomena in real time, providing a solid foundation for subsequent analysis.

[0053] Next, the image analysis unit uses advanced meteorological models to conduct in-depth analysis of the collected radar satellite images. This unit not only has the ability to perform preliminary pre-processing of images, such as denoising and contrast enhancement, but also embeds a highly specialized meteorological model. The model is able to simulate the future weather conditions in the area where the building is located through complex calculations based on the cloud density, movement speed, height and other relevant parameters in the atmosphere shown in the image. This process involves a lot of data processing and pattern recognition technology, aiming to accurately extract weather parameters that are directly related to building safety, such as wind speed, wind direction, rainfall, temperature, etc. That is, the weather parameters of the area where the building belongs are obtained.

[0054] Specifically, in this embodiment, the image analysis unit performs preliminary preprocessing on the image, including: PSF detection: The on-orbit PSF detection of high-resolution optical remote sensing satellites based on sub-pixel targets is a direct detection method based on the physical definition of the point spread function. A parameterized point spread model is used to perform surface fitting on each sub-pixel target response data to determine the peak position of each target response, and then the sub-pixel target response value or count value (DN value) of the 3×3 array with non-integer pixel intervals is aligned to obtain the point spread response of the sub-pixel interpolation, so as to reduce the influence of system sampling effect and random noise.

[0055] In this embodiment, the construction of the meteorological simulation embedded in the image analysis unit specifically includes: constructing a meteorological model based on a logistic regression model, specifically, collecting meteorological-related data, including meteorological observation data such as temperature, humidity, air pressure, wind speed, and other meteorological-related data, such as geographical location, season, etc. These data are obtained from sources such as meteorological stations, meteorological satellites, sensors, etc.

[0056] Preprocess the collected data, including data cleaning, missing value processing, outlier processing, etc. Ensure the quality and integrity of the data.

[0057] According to the goal of the meteorological model, select the characteristic variables related to model construction from the collected data. Select and extract key features through statistical analysis, feature engineering and other methods.

[0058] Divide the dataset into a training set and a test set. Usually most of the data is used to train the model, and a small part of the data is used to evaluate the performance of the model.

[0059] The logistic regression algorithm is selected as the basic model, and the model is trained based on the meteorological data and corresponding labels (such as weather conditions) in the training set. Logistic regression is a binary classification algorithm that can predict the probability that a sample belongs to a certain category based on the input feature value.

[0060] Use the test set to evaluate the performance of the model. Use indicators such as accuracy, precision, recall, and F1 score to evaluate the prediction effect of the model.

[0061] According to the evaluation results, the model is optimized. The model performance can be improved by adjusting the model's hyperparameters, introducing regularization techniques, and trying other classification algorithms.

[0062] After obtaining the meteorological model, clouds, storms and atmospheric movements are obtained based on the clarified satellite images; precipitation, storm intensity and location are obtained based on radar images, and the above information is input into the meteorological model constructed through the logistic regression model to predict weather changes, namely weather parameters.

[0063] Specifically, in this embodiment, the prediction subunit is used to predict the light intensity and ambient temperature of the preset time period using the prediction algorithm model according to the predicted weather changes, i.e., weather parameters, that is, to predict the power generation influencing factors at different times in the future preset time period, and the light intensity and ambient temperature are the prediction results. Finally, the prediction results are fused and analyzed according to the multimodal neural network embedded in the fusion analysis subunit to obtain the power generation at different times in the preset time period.

[0064] Furthermore, the power generation unit specifically utilizes the photovoltaic effect to convert solar energy into electrical energy and directly supplies power to the building.

[0065] Furthermore, the power generation module also includes a cleaning unit; the cleaning unit is used to clean the panel of the power generation unit.

[0066] Specifically, in a further optimized embodiment, the power generation unit, as the energy conversion core of the entire system, adopts the efficient photovoltaic effect principle to directly convert abundant solar energy resources into electrical energy. This conversion process is not only clean and environmentally friendly, but also has significant advantages such as strong sustainability and wide application range. Since the solar panel is exposed to the outdoor environment for a long time, it is easy to accumulate impurities such as dust, dirt, bird droppings, etc., which will seriously affect the light absorption efficiency and power conversion efficiency of the panel. In order to ensure that the photovoltaic power generation unit can continue to maintain its high-efficiency power generation state, this embodiment adds an important component, the cleaning unit. The main task of the cleaning unit is to perform deep cleaning of the solar panel of the power generation unit regularly or according to actual needs. The cleaning unit specifically adopts automated cleaning technology, accurately locates the contaminated area through an intelligent control system, and uses physical methods such as soft cleaning brushes or high-pressure gas to effectively remove various pollutants without damaging the surface of the panel. In addition, the cleaning unit also has self-monitoring and maintenance functions, which can detect the cleaning effect in real time, and adjust the cleaning strategy and frequency as needed to ensure that the solar panel always maintains the best working condition.

[0067] Furthermore, the energy storage module includes an alarm device and multiple energy storage devices; the alarm device is used to issue an alarm when the storage capacity of the multiple energy storage devices is lower than a preset value; the multiple energy storage devices are used to supply power to the power-consuming devices in sequence using an automatic switching mechanism.

[0068] Specifically, the energy storage device in this embodiment includes a first energy storage device and a second energy storage device. The first energy storage device is used to store the electric energy converted by the power generation unit, and when the power generation unit is unable to supply power to the ultra-low energy consumption building due to weather reasons or system failure, it automatically switches to the first energy storage device to supply power to the ultra-low energy consumption building, ensuring that the power demand of the building is met.

[0069] The second energy storage device serves as a backup power source, storing the electric energy converted by the power generation unit, and taking over the task of supplying power to the ultra-low energy consumption building when the power generation unit, the first energy storage device and the mains (in this embodiment, the mains is specially connected as an additional power source to ensure the stability of the power supply of the ultra-low energy consumption building) are unable to supply power to the ultra-low energy consumption building. This hierarchical energy storage and power supply design greatly improves the reliability and stability of the system.

[0070] The alarm device is designed to alert management personnel at critical moments to draw attention to the operating status of the system. The device can operate according to a preset power storage threshold, and once it detects that the remaining power of the energy storage module is lower than this preset value, it will immediately trigger an alarm. Specifically, in the practical application of this embodiment, when the second energy storage device begins to supply power to the ultra-low energy consumption building, it indicates that the system's power reserve is close to the limit. At this time, the alarm device will respond quickly and send out a clear and eye-catching alarm signal. The issuance of this signal is intended to remind management personnel that they must take immediate action, such as starting the emergency power supply system, or conducting a comprehensive inspection and troubleshooting of the system to ensure that the power supply of the ultra-low energy consumption building is not affected, thereby maintaining its normal operation.

[0071] Furthermore, the power distribution module includes a distribution unit and a control unit; the distribution unit is used to plan a power distribution plan based on the power generation results and the operating characteristics of the electrical equipment in the building, and determine the power distribution parameters based on the power distribution plan; the control unit is used to control the start and stop and power quota of the electrical equipment in the building according to the power distribution parameters.

[0072] Specifically, the power distribution module is responsible for planning the optimal power distribution plan based on the predicted power generation results and the operating characteristics of each power device in the building. In this process, the distribution unit will comprehensively consider multiple factors, such as the output power of the power generation unit, the remaining power of the energy storage module, the energy consumption characteristics of each power device and its operating priority. Through complex algorithm calculations, the distribution unit can accurately determine the power distribution parameters of each power device in different time periods, including but not limited to voltage, current, power factor and power supply time. The control unit performs precise start and stop control and power quota management on the power devices in the building according to the power distribution parameters provided by the distribution unit. Specifically, the control unit will monitor the working status of each power device in real time, and automatically adjust its power supply parameters according to the requirements of the power distribution plan to ensure that each device can obtain the required power resources within a given time. At the same time, during peak power consumption or power shortage, the control unit can also intelligently adjust the power quota of each device to give priority to the normal operation of key equipment, thereby achieving reasonable allocation and efficient utilization of power resources.

[0073] Furthermore, the distribution unit includes a matching subunit and a parameter determination subunit; the matching subunit is used to match the operating characteristics of the power-consuming device and the power generation results predicted by the power generation module based on a genetic algorithm; the parameter determination subunit is used to plan a power distribution plan according to the matching results, and determine the power distribution parameters according to the power distribution plan.

[0074] Specifically, the allocation unit in the power distribution module is further refined into a matching subunit and a parameter determination subunit, which together realize efficient and intelligent allocation of power resources.

[0075] As the front end of the distribution unit, the core task of the matching subunit is to accurately match the operating characteristics of each electrical device in the building with the power generation results predicted by the power generation module based on advanced genetic algorithms. In this process, the matching subunit will fully consider key information such as the energy consumption pattern, operating priority, and power demand fluctuations of the electrical devices, and at the same time combine the real-time power generation capacity of the power generation module and the power generation forecast for a period of time in the future to construct a multi-variable, multi-constraint optimization problem. Through the powerful search capability of the genetic algorithm, the matching subunit can find the optimal or nearly optimal matching solution in a complex solution space.

[0076] The parameter determination subunit is responsible for planning the specific power distribution plan based on the output result of the matching subunit, that is, the optimal matching plan. In this step, the parameter determination subunit will calculate in detail the power demand of each electrical device in different time periods, including key parameters such as voltage level, current size, power factor, etc., and determine the power distribution strategy of the entire building power system accordingly. Through precise parameter setting, the parameter determination subunit can ensure that power resources can minimize overall energy consumption and maximize power efficiency while meeting the needs of each electrical device. Furthermore, it also includes an optimization subunit; the optimization subunit is used to optimize the genetic algorithm, and perform iterative matching calculations based on the optimized genetic algorithm to select matching results that meet the preset requirements.

[0077] In order to further improve the accuracy and efficiency of matching, this embodiment also specifically introduces an optimization subunit. The optimization subunit is mainly used to adaptively update the old task parameters and new task parameters of the genetic algorithm through an adaptive method to obtain an optimized genetic algorithm. The optimization specifically includes adjusting the algorithm parameters, improving the search strategy, introducing new genetic operations, etc., to improve the performance and stability of the algorithm when solving complex optimization problems. Through the optimized genetic algorithm, the optimization subunit can perform multiple iterative matching calculations and continuously screen out better matching results until the preset matching accuracy and efficiency requirements are met. This process not only improves the intelligence level of the power distribution module, but also provides a more solid guarantee for the stable operation of the building power system.

[0078] Among them, the method of obtaining the optimal matching solution through the optimized genetic algorithm is as follows:

[0079] The solution to the power distribution problem is represented as a chromosome through real number coding. The genes on the chromosome represent the power distribution of the power consumption devices;

[0080] Create an initial population where each individual is a possible solution for power distribution. These individuals are generated based on prior knowledge;

[0081] The quality of each individual is evaluated through the objective function, and the goal is to maximize efficiency. The higher the value of the objective function, the better the matching solution;

[0082] The selection operation is performed using the roulette wheel selection method to select better individuals as parents so that they can pass on their genetic information to the offspring;

[0083] Perform a crossover operation on the selected parent individuals to generate new individuals;

[0084] The newly generated individuals are mutated to introduce some randomness and avoid local optimal solutions. The mutation method uses real-valued mutation, that is, making small adjustments to the values ​​in the chromosomes;

[0085] Calculate the objective function value for the newly generated individuals and then compare them with the individuals in the existing population. Select members of the next generation population;

[0086] Define a maximum number of iterations to terminate the iteration;

[0087] Analyze the output of the algorithm to find the optimal matching result for power distribution.

[0088] Subsequently, the parameter determination subunit will carefully plan a specific power distribution plan based on the optimal matching result obtained by the optimization subunit. Based on this power distribution plan, the parameter determination subunit will carefully determine the power distribution parameters required for each electrical device. In particular, in the actual application scenario of this embodiment, we introduced the key component of the transmission unit. The main responsibility of the transmission unit is to accurately transmit these precisely calculated power distribution parameters to the control unit to ensure that the control unit can accurately distribute and manage power to the electrical equipment according to the established power distribution plan.

[0089] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and descriptions are only preferred examples of the present invention and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. An intelligent control system for building electricity based on renewable energy generation, characterized in that: include: Power generation modules, energy storage modules and power distribution modules; The power generation module is arranged on the outer surface of the building, and is used to convert the collected renewable energy into electrical energy, supply power to the electrical devices in the building, and to predict the power generation results within a preset time period; The energy storage module is connected to the power generation module, and is used to store the electric energy converted by the power generation module and supply power to the electric device under preset conditions; The power distribution module is used to plan a power distribution plan according to the power generation result, and control the start and stop and power consumption quota of the power-consuming device according to the power distribution plan.

2. The intelligent control system for building electricity according to claim 1 is characterized in that: The power distribution module includes a distribution unit and a control unit; The distribution unit is used to plan a power distribution plan according to the power generation results and the operating characteristics of the electrical devices in the building, and determine the power distribution parameters according to the power distribution plan; The control unit is used to control the start and stop and power consumption quota of the electrical devices in the building according to the power distribution parameters.

3. The intelligent control system for building electricity according to claim 2 is characterized in that: The allocation unit includes a matching subunit and a parameter determination subunit; The matching subunit is used to match the operating characteristics of the power-consuming device and the power generation result predicted by the power generation module based on a genetic algorithm; The parameter determination subunit is used to plan a power distribution plan according to the matching result, and determine the electric energy distribution parameters according to the power distribution plan.

4. The intelligent control system for building electricity according to claim 3 is characterized in that: It also includes an optimization subunit; The optimization subunit is used to optimize the genetic algorithm, and perform iterative matching calculations according to the optimized genetic algorithm to select matching results that meet preset requirements.

5. The intelligent control system for building electricity according to claim 1 is characterized in that: The power generation module includes a power generation prediction unit and a power generation unit; The power generation prediction unit is used to predict the power generation at different times within a preset period according to the weather parameters of the area to which the building belongs, and record the power generation at different times within the preset period as the power generation result; The power generation unit is used to convert the collected renewable energy into electrical energy.

6. The intelligent control system for building electricity according to claim 5 is characterized in that: The power generation prediction unit includes a data acquisition subunit, a prediction subunit and a fusion analysis subunit; The data acquisition subunit is used to collect weather parameters of the area to which the building belongs; The prediction subunit is used to predict the power generation influencing factors at different times in a future preset period of time according to the weather parameters to obtain a prediction result; The fusion analysis subunit is used to perform fusion analysis on the prediction results according to the multimodal neural network to obtain the power generation at different times within a preset time period.

7. The intelligent control system for building electricity according to claim 6, characterized in that: The data acquisition subunit includes an image acquisition unit and an image analysis unit; The image acquisition unit is used to collect radar satellite images of clouds and atmospheric motion; The image analysis unit is used to perform meteorological simulation on the radar satellite image based on a meteorological model to obtain weather parameters of the area to which the building belongs.

8. The intelligent control system for building electricity according to claim 5, characterized in that: The power generation unit specifically utilizes the photovoltaic effect to convert solar energy into electrical energy.

9. The intelligent control system for building electricity according to claim 8, characterized in that: The power generation module also includes a cleaning unit; The cleaning unit is used to clean the panel of the power generation unit.

10. The intelligent control system for building electricity according to claim 1, characterized in that: The energy storage module includes an alarm device and a plurality of energy storage devices; The alarm device is used to issue an alarm when the amount of power stored in the multiple energy storage devices is lower than a preset value; The multiple energy storage devices are used to supply power to the electrical devices in sequence using an automatic switching mechanism.

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