A Hybrid Load Forecasting Method Based on Neural Network in Smart Grid

Short-term power load forecasting is very important for the electric power market, and the forecasting method should have high accuracy and high speed. A three-layer BP neural network has the ability to approximate any N-dimensional continuous function with arbitrary precision. In this paper, a short-term power load forecasting method based on BP neural network is proposed. This method uses the three-layer neural network with single hidden layer as forecast model. In order to improve the training speed of BP neural network and the forecasting efficiency, this method firstly reduces the factors which affect load forecasting by using rough set theory, then takes the reduced data as input variables of the BP neural network model, and gets the forecast value by using back-propagation algorithm. The forecasting results with real data show that the proposed method has high accuracy and low complexity in short-term power load forecasting.

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Optimization of Load Forecasting in Smartgrid using Artificial Neural Network based NFTOOL and NNTOOL

Journal of Physics Conference Series ◽

10.1088/1742-6596/2161/1/012068 ◽

2022 ◽

Vol 2161 (1) ◽

pp. 012068

Author(s):

Sthitprajna Mishra ◽

Bibhu Prasad Ganthia ◽

Abel Sridharan ◽

P Rajakumar ◽

D. Padmapriya ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Smart Grid ◽

User Interfaces ◽

Load Forecasting ◽

Back Propagation ◽

Back Propagation Algorithm ◽

Load Prediction ◽

Artificial Neural ◽

Optimal Reserve

Abstract The motivation behind the research is the requirement of error-free load prediction for the power industries in India to assist the planners for making important decisions on unit commitments, energy trading, system security & reliability and optimal reserve capacity. The objective is to produce a desktop version of personal computer based complete expert system which can be used to forecast the future load of a smart grid. Using MATLAB, we can provide adequate user interfaces in graphical user interfaces. This paper devotes study of load forecasting in smart grid, detailed study of architecture and configuration of Artificial Neural Network(ANN), Mathematical modeling and implementation of ANN using MATLAB and Detailed study of load forecasting using back propagation algorithm.

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Short-Term Load Forecasting Method Based on Deep Reinforcement Learning for Smart Grid

Mobile Information Systems ◽

10.1155/2021/8453896 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Wei Guo ◽

Kai Zhang ◽

Xinjie Wei ◽

Mei Liu

Keyword(s):

Reinforcement Learning ◽

Smart Grid ◽

Prediction Model ◽

Network Model ◽

Power Grid ◽

Load Forecasting ◽

Model Parameters ◽

Short Term ◽

Forecasting Method ◽

Short Term Load Forecasting

Short-term load forecasting is an important part to support the planning and operation of power grid, but the current load forecasting methods have the problem of poor adaptive ability of model parameters, which are difficult to ensure the demand for efficient and accurate power grid load forecasting. To solve this problem, a short-term load forecasting method for smart grid is proposed based on multilayer network model. This method uses the integrated empirical mode decomposition (IEMD) method to realize the orderly and reliable load state data and provides high-quality data support for the prediction network model. The enhanced network inception module is used to adaptively adjust the parameters of the deep neural network (DNN) prediction model to improve the fitting and tracking ability of the prediction network. At the same time, the introduction of hybrid particle swarm optimization algorithm further enhances the dynamic optimization ability of deep reinforcement learning model parameters and can realize the accurate prediction of short-term load of smart grid. The simulation results show that the mean absolute percentage error e MAPE and root-mean-square error e RMSE of the performance indexes of the prediction model are 10.01% and 2.156 MW, respectively, showing excellent curve fitting ability and load forecasting ability.

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