Centralized smart energy load regulation and control system based on public building

Abstract

The invention relates to the technical field of energy scheduling, and discloses a centralized smart energy load regulation and control system based on a public building, which comprises a power supply unit, a central control unit, a data acquisition unit, a scheduling execution unit and an equipment classification unit, the central control unit comprises a data processing module and an energy consumption demand prediction module. By classifying the equipment and evaluating the energy efficiency, the energy consumption characteristics and efficiency levels of different equipment can be clearly known, an accurate regulation and control strategy can be formulated for each category, the equipment with low energy efficiency can be emphatically optimized, the overall energy utilization efficiency can be improved, demand prediction is carried out by virtue of previous energy consumption data, the regulation and control are more prospective, and the energy efficiency of the equipment is improved. The energy consumption trend of different time periods and seasons can be predicted in advance, so that excessive investment on electric power infrastructures is avoided, the electric power cost is saved, limited electric power resources are preferentially allocated to key equipment and important areas, and optimal allocation of the resources is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of energy dispatching, and in particular to a centralized intelligent energy load control system based on public buildings. Background Art

[0002] Energy regulation of public buildings refers to the process of scientifically managing and optimizing the use of various types of energy in public buildings. It covers the comprehensive management and control of electricity, gas, water and other energy sources. By installing intelligent metering equipment, real-time monitoring of energy consumption can be obtained to obtain accurate data. Based on these data, advanced control systems and strategies can be used to reasonably dispatch equipment in the building, such as air conditioners, lighting, elevators, etc. During peak hours, the energy consumption of non-critical equipment can be appropriately reduced, or equipment operating parameters can be adjusted to improve energy efficiency.

[0003] When the control systems in public buildings regulate electricity according to the electricity energy consumption demand in the area, many existing control systems find it difficult to fully integrate equipment of different types and brands. Public buildings often use multiple brands and models of electrical equipment, and some control systems have difficulties in integrating the data collection and control functions of these devices, resulting in the inability to comprehensively and accurately regulate the overall electricity of the building. In addition, most existing control systems perform real-time regulation based on current or historical data and lack the ability to effectively predict future electricity demand. Summary of the invention

[0004] The purpose of the present invention is to provide a centralized intelligent energy load control system based on public buildings, which can effectively solve the problems in the background technology.

[0005] To achieve the above object, the technical solution adopted by the present invention is:

[0006] A centralized intelligent energy load control system based on public buildings includes a power supply unit, a central control unit, a data acquisition unit, a scheduling execution unit and equipment classification. The power supply unit includes a municipal power grid and a solar power generation module. The central control unit includes a data processing module and an energy consumption demand prediction module. The data acquisition unit includes a regional energy consumption metering module.

[0007] As a further preferred embodiment of the present invention, the municipal power grid is connected to the peak-valley power conversion module to directly access the user and a municipal power grid input / input monitoring module is set. The municipal power grid input / input monitoring module is unidirectionally connected to the data acquisition unit. The data acquisition unit obtains real-time data such as voltage, current, power factor, frequency, etc. of the municipal power grid, as well as cumulative power consumption, power consumption data during peak and valley power periods, etc. from the municipal power grid input / input monitoring module.

[0008] As a further preferred embodiment of the present invention, the solar power generation module is connected to a centralized energy storage power supply in a building, and a solar output monitoring module is arranged between the solar power generation module and the energy storage power supply to collect the output current and voltage of the solar power generation module, so as to calculate data such as real-time power and daily power generation. At the same time, environmental parameters such as the operating temperature of the solar panel can also be monitored to evaluate its power generation efficiency. The energy storage power supply is connected to the municipal power grid and the user, and an energy storage power supply input / output monitoring module is connected to the charging and discharging circuit of the energy storage power supply. The energy storage power supply input / output monitoring module is unidirectionally connected to a data acquisition unit, and the data acquisition unit obtains information such as the charging and discharging current, voltage, power and remaining power of the energy storage power supply, so as to grasp the energy storage status and charging and discharging conditions in real time.

[0009] As a further preferred embodiment of the present invention, the regional energy consumption metering module includes a plurality of sensors, which are respectively arranged on power supply lines of a plurality of regions and are unidirectionally connected to the data acquisition unit.

[0010] As a further preferred embodiment of the present invention, after the equipment is classified, energy efficiency evaluation is performed on the statistically counted equipment, and the equipment energy efficiency evaluation information is sent to the energy consumption demand prediction module through the data processing module. The energy consumption demand prediction module establishes a prediction model based on the energy efficiency evaluation data, which includes the following steps:

[0011] S1. Data cleaning: remove outliers in the data, such as obviously unreasonable energy consumption data points caused by equipment failure or data collection errors, and fill in missing data. Mean, median filling or interpolation-based methods can be used.

[0012] S2. Data standardization: standardize variables of different magnitudes and use the Z-score standardization method to make each variable within the same magnitude range to facilitate subsequent model processing.

[0013] S3. Select the linear regression model, Y = B0 + B1X1 + B2X2... + B n X n +E,Y is the energy demand, X i is the selected feature, B i is the coefficient to be determined, E is the error term, and for the linear regression model, the least squares method can be used to solve the model coefficient B i .

[0014] S4. Selection of evaluation indicators: Mean square error (MSE), mean absolute error (MAE) or coefficient of determination (R 2 ) and other indicators to evaluate the performance of the model on the test set.

[0015] As a further preferred embodiment of the present invention, the energy consumption demand prediction module transmits the prediction and then feeds back the prediction information to the data processing module, and simultaneously displays the prediction information to the data visualization device.

[0016] As a further preferred embodiment of the present invention, the central control unit cooperates with the data acquisition unit to respectively obtain the output power of the solar power generation module, the input / output power of the energy storage power supply, and the input power of the municipal power grid, and performs data processing based on the data collected by the regional energy consumption metering module, and analyzes the energy consumption through the data analysis module. When the energy consumption of the corresponding area is greater than the energy consumption demand forecast of the area, the early warning information is displayed through the early warning module box data visualization device, and synchronously fed back to the data processing module. The energy consumption demand data of the current area is compared with the previous data through the data processing module to determine whether the energy consumption demand of the current area is abnormal, and the power supply of the current area is stopped through optimized scheduling and confirmed through manual review to increase the power supply of the current area.

[0017] As a further preferred embodiment of the present invention, the data collected by the regional metering module is uploaded to the data processing module via the data acquisition unit and then analyzed by the data analysis module. When the regional energy consumption demand is small, the input of the municipal power grid is reduced and the output of the energy storage power supply is increased. When the energy storage power supply is sufficient and the output of the solar power generation module is greater than the input to the energy storage power supply and the user end, the solar power generation module is reversed to the municipal power grid and outputted. When the input of the energy storage power supply is insufficient and the output of the solar power generation module is insufficient, the input of the municipal power grid is increased. When it is in the valley power time period, the central control unit uses the municipal power grid to cooperate with the peak-to-valley power conversion module to input to the energy storage power supply.

[0018] Compared with the prior art, the present invention has the following beneficial effects:

[0019] In the present invention, by classifying and evaluating the energy efficiency of equipment, the energy consumption characteristics and efficiency levels of different equipment can be clearly understood, and accurate control strategies can be formulated for each category. Equipment with low energy efficiency can be optimized to improve the overall energy utilization efficiency. Demand forecasting can be carried out with the help of past energy consumption data to make the control more forward-looking, and energy consumption trends in different time periods and seasons can be predicted in advance, thereby avoiding excessive investment in power infrastructure, saving electricity costs, and allocating limited power resources to key equipment and important areas in priority, thereby achieving optimal resource allocation. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a schematic diagram of the detailed explanation process of the present invention. DETAILED DESCRIPTION

[0021] In order to make the technical means, creative features, objectives and effects achieved by the present invention easy to understand, the present invention is further explained below in conjunction with specific implementation methods.

[0022] like Figure 1 As shown, the present invention provides a centralized intelligent energy load control system based on public buildings, including a power supply unit, a central control unit, a data acquisition unit, a scheduling execution unit and equipment classification. The power supply unit includes a municipal power grid and a solar power generation module, the central control unit includes a data processing module and an energy consumption demand prediction module, and the data acquisition unit includes a regional energy consumption metering module.

[0023] The municipal power grid is connected to the peak-valley power conversion module, which is directly connected to the user and sets up the municipal power grid input\input monitoring module. The municipal power grid input\input monitoring module is unidirectionally connected to the data acquisition unit. The data acquisition unit obtains the real-time data of the municipal power grid such as voltage, current, power factor, frequency, etc., as well as the cumulative power consumption, power consumption data during peak and valley power periods, etc. from the municipal power grid input\input monitoring module; the electricity price during valley power periods is lower, and users can use the conversion module to store more valley power for peak power periods, which can significantly reduce electricity costs. The solar power generation module is connected to the centralized energy storage power supply in the building, and a solar output monitoring module is set between the solar power generation module and the energy storage power supply to collect the output current and voltage of the solar power generation module, so as to calculate the real-time power and daily power generation data. At the same time, it can also monitor the environmental parameters such as the working temperature of the solar panel to evaluate its power generation efficiency. The energy storage power supply is connected to the municipal power grid and the user, and the energy storage power supply input\output monitoring module is connected to the charging and discharging circuit of the energy storage power supply. The energy storage power supply input\output monitoring module is connected to the data acquisition The data collection unit is unidirectionally connected, and the data acquisition unit obtains the charging and discharging current, voltage, power and remaining power of the energy storage power supply, and grasps the energy storage state and charging and discharging status in real time. The regional energy consumption metering module includes multiple sensors, which are respectively arranged on the power supply lines of multiple regions and are unidirectionally connected to the data acquisition unit. After the equipment is classified, the energy efficiency of the statistical equipment is evaluated, and the equipment energy efficiency evaluation information is sent to the energy consumption demand prediction module through the data processing module. The energy consumption demand prediction module establishes a prediction model according to the energy efficiency evaluation data, which includes the following steps: data cleaning, removing abnormal values ​​in the data, such as obviously unreasonable energy consumption data points caused by equipment failure or data collection errors, filling missing data, and using mean, median filling or interpolation-based methods. Data standardization, standardizing variables of different magnitudes, using the Z-score standardization method to make each variable in the same magnitude range, which is convenient for subsequent model processing, and selecting a linear regression model, Y = B0 + B1X1 + B2X2... + B n X n +E,Y is the energy demand, X i is the selected feature, B iis the coefficient to be determined, E is the error term, and for the linear regression model, the least squares method can be used to solve the model coefficient B i , the evaluation index selection is the mean square error (MSE), mean absolute error (MAE) or determination coefficient (R 2 ) and other indicators to evaluate the performance of the model on the test set. The energy consumption demand prediction module transmits the prediction and feeds back the prediction information to the data processing module, and displays the prediction information to the data visualization device at the same time. The central control unit cooperates with the data acquisition unit to obtain the output power of the solar power generation module, the input\output power of the energy storage power supply, and the input power of the municipal power grid, and processes the data according to the data collected by the regional energy consumption metering module, and analyzes the energy consumption through the data analysis module. When the energy consumption of the corresponding area is greater than the energy consumption demand forecast of the area, the early warning information is displayed through the early warning module box data visualization device, and synchronously fed back to the data processing module. The data processing module compares the energy consumption demand data of the current area with the previous data to determine the energy consumption demand of the current area. The system checks whether there is an abnormality, stops the power supply of the current area through optimized scheduling, and confirms it through manual review to increase the power supply of the current area. The data collected by the regional metering module is uploaded to the data processing module through the data acquisition unit, and then analyzed by the data analysis module. When the regional energy consumption demand is small, the input of the municipal power grid is reduced, and the output of the energy storage power supply is increased. When the energy storage power supply is sufficient and the output of the solar power generation module is greater than the input to the energy storage power supply and the user end, the solar power generation module is reversed to the municipal power grid and output. When the input of the energy storage power supply is insufficient and the output of the solar power generation module is insufficient, the input of the municipal power grid is increased. When it is in the valley power period, the central control unit uses the municipal power grid to cooperate with the peak-to-valley power conversion module to input to the energy storage power supply.

[0024] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of 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 to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A centralized intelligent energy load control system based on public buildings, characterized by: It includes a power supply unit, a central control unit, a data acquisition unit, a scheduling execution unit and a data visualization device. The equipment is classified as follows: the power supply unit includes a municipal power grid and a solar power generation module, the central control unit includes a data processing module and an energy consumption demand prediction module, and the data acquisition unit includes a regional energy consumption metering module.

2. According to claim 1, a centralized intelligent energy load control system based on public buildings is characterized in that: The municipal power grid is connected to the peak-valley power conversion module to directly access the user and set up a municipal power grid input\input monitoring module. The municipal power grid input\input monitoring module is unidirectionally connected to the data acquisition unit. The data acquisition unit obtains real-time data such as voltage, current, power factor, frequency, etc. of the municipal power grid from the municipal power grid input\input monitoring module, as well as cumulative power consumption, power consumption data during peak and valley power periods, etc.

3. According to claim 1, a centralized intelligent energy load control system based on public buildings is characterized in that: The solar power generation module is connected to a centralized energy storage power supply in a building, and a solar output monitoring module is arranged between the solar power generation module and the energy storage power supply to collect output current and voltage of the solar power generation module, so as to calculate data such as real-time power and daily power generation. At the same time, environmental parameters such as the working temperature of the solar panel can also be monitored to evaluate its power generation efficiency. The energy storage power supply is connected to the municipal power grid and the user. The energy storage power supply input / output monitoring module is connected to the charging and discharging circuit of the energy storage power supply. The energy storage power supply input / output monitoring module is unidirectionally connected to the data acquisition unit. The data acquisition unit obtains information such as the charging and discharging current, voltage, power and remaining power of the energy storage power supply, so as to grasp the energy storage status and charging and discharging conditions in real time.

4. According to claim 1, a centralized intelligent energy load control system based on public buildings is characterized in that: The regional energy consumption metering module includes a plurality of sensors, which are respectively arranged on power supply lines of a plurality of regions and are unidirectionally connected to a data acquisition unit.

5. The centralized intelligent energy load control system based on public buildings according to claim 1 is characterized in that: After the equipment is classified, energy efficiency evaluation is performed on the statistically counted equipment, and the equipment energy efficiency evaluation information is sent to the energy consumption demand prediction module through the data processing module. The energy consumption demand prediction module establishes a prediction model according to the energy efficiency evaluation data, which includes the following steps: S1. Data cleaning: remove outliers in the data, such as obviously unreasonable energy consumption data points caused by equipment failure or data collection errors, and fill in missing data. Mean, median filling or interpolation-based methods can be used. S2. Data standardization: standardize variables of different magnitudes and use the Z-score standardization method to make each variable within the same magnitude range to facilitate subsequent model processing. S3. Select the linear regression model, Y = B0 + B1X1 + B2X2... + B n X n +E,Y is the energy demand, X i is the selected feature, B i is the coefficient to be determined, E is the error term, and for the linear regression model, the least squares method can be used to solve the model coefficient B i . S4. Selection of evaluation indicators: Mean square error (MSE), mean absolute error (MAE) or coefficient of determination (R 2 ) and other indicators to evaluate the performance of the model on the test set.

6. The centralized intelligent energy load control system based on public buildings according to claim 5 is characterized in that: The energy consumption demand prediction module transmits the prediction and then feeds back the prediction information to the data processing module, and at the same time displays the prediction information to the data visualization device.

7. The centralized intelligent energy load control system based on public buildings according to claim 6 is characterized by: The central control unit cooperates with the data acquisition unit to respectively obtain the output power of the solar power generation module, the input / output power of the energy storage power supply, and the input power of the municipal power grid, and performs data processing based on the data collected by the regional energy consumption metering module, and analyzes the energy consumption through the data analysis module. When the energy consumption of the corresponding area is greater than the energy consumption demand forecast of the area, the early warning information is displayed through the early warning module box data visualization device, and synchronously fed back to the data processing module. The energy consumption demand data of the current area is compared with the previous data through the data processing module to determine whether the energy consumption demand of the current area is abnormal, and the power supply of the current area is stopped through optimized scheduling and confirmed through manual review to increase the power supply of the current area.

8. A centralized intelligent energy load control system based on public buildings according to claim 7, characterized in that: The data collected by the regional metering module is uploaded to the data processing module via the data acquisition unit and then analyzed by the data analysis module. When the regional energy consumption demand is small, the input of the municipal power grid is reduced and the output of the energy storage power supply is increased. When the energy storage power supply is sufficient and the output of the solar power generation module is greater than the input to the energy storage power supply and the user end, the solar power generation module is reversed to the municipal power grid and outputted. When the input of the energy storage power supply is insufficient and the output of the solar power generation module is insufficient, the input of the municipal power grid is increased. When it is in the valley power period, the central control unit uses the municipal power grid to cooperate with the peak-to-valley power conversion module to input to the energy storage power supply.

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