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Building electrical load comprehensive prediction method and system based on LSTM neural network

A neural network and comprehensive prediction technology, applied in biological neural network models, predictions, neural architectures, etc., can solve problems such as difficulty in providing prediction accuracy, and achieve the effect of easy implementation and high accuracy

Pending Publication Date: 2020-08-21
SHANDONG ELECTRIC POWER ENG CONSULTING INST CORP
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0009] The above methods play a certain role in building load planning, but it is difficult to provide more accurate prediction accuracy for complex and changeable building types and uncertain historical data

Method used

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  • Building electrical load comprehensive prediction method and system based on LSTM neural network
  • Building electrical load comprehensive prediction method and system based on LSTM neural network
  • Building electrical load comprehensive prediction method and system based on LSTM neural network

Examples

Experimental program
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Embodiment 1

[0035] See attached figure 2 As shown, this embodiment discloses a comprehensive prediction method for building electrical load based on LSTM neural network. Building electrical load planning is the most important in comprehensive energy planning. The electrical load conditions of different geographical locations and different types of buildings are not only It is not the same, but also need to consider various influencing factors such as building area, climate conditions in the area where the building is located, flow of people in the building, work and rest time, and weather factors.

[0036] Power consumption data include: inherent information such as building type, building area, functional zoning, geographical location, and climate conditions, building flow of people, work and rest time, and corresponding meteorological data (including temperature, humidity, etc.), historical load information including electric load curves, electricity consumption, etc. curve. The LSTM ...

Embodiment 2

[0086] Based on the same inventive concept, the purpose of this embodiment is to provide a computing device, which includes a memory, a processor, and a computer program stored in the memory and operable on the processor. The steps of a comprehensive forecasting method for building electrical load based on LSTM neural network in Example 1.

Embodiment 3

[0088] Based on the same inventive concept, the purpose of this embodiment is to provide a computer-readable storage medium.

[0089] A computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the steps of the method for building electrical load comprehensive forecasting based on LSTM neural network in Embodiment 1 are executed.

[0090] Embodiment three

[0091] Based on the same inventive concept, the purpose of this embodiment is to provide a building electrical load comprehensive forecasting system based on LSTM neural network.

[0092] A building electrical load comprehensive forecasting system based on LSTM neural network, including:

[0093] Data processing module: obtain load data, weather parameters and building data of typical buildings and perform normalization processing;

[0094] LSTM forecasting model training module: establish the electrical load forecasting model of the LSTM neural network, dete...

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Abstract

The invention discloses a building electrical load comprehensive prediction method and system based on an LSTM neural network. The method comprises the steps of obtaining load data, weather parametersand building data of a typical building and performing normalization processing; establishing an electrical load prediction model of the LSTM neural network, selecting data of similar typical days astraining samples, wherein the training data comprises weather factors of the training days, building type data and load data, and training is carried out with the minimum electrical load error as thetarget in the training process to obtain LSTM neural network model parameters; and inputting building data of a to-be-tested building to the trained electrical load prediction model of the LSTM neural network, and then obtaining a typical daily load curve, a monthly load curve and an annual load curve corresponding to the building. According to the building load prediction method based on the LSTM neural network, different characteristics and load fluctuation change conditions of a building are comprehensively considered to realize high-precision load prediction of the building, and the method has the functions of high precision and easy realization.

Description

technical field [0001] The invention belongs to the technical field of load forecasting, and in particular relates to a comprehensive forecasting method and system for building electrical loads based on LSTM neural network. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art. [0003] Integrated smart energy is a kind of distributed energy, and its economical operation largely depends on whether the system configuration is optimized, and the calculation of electricity, heat, and cooling loads is the basis for the optimal configuration of the system, and the closeness of the design load to the actual operating load , directly determines the rationality of system configuration and the economy of operation. [0004] Building electrical load forecasting is based on the historical data of similar building electrical load, weather, building information, etc...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/08G06N3/04
CPCG06Q10/04G06Q50/08G06N3/044G06N3/045Y04S10/50
Inventor 任其文魏华栋尹晓东朱月涌卢静樊潇于明辉贺艳辉杨猛
Owner SHANDONG ELECTRIC POWER ENG CONSULTING INST CORP
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