Short-Term Power Load Forecasting via Recurrent Neural Network with Similar Day Selection

2021 ◽  
Author(s):  
Ao Liu ◽  
Jiangnan Li ◽  
Yiying Che ◽  
Bin Qian ◽  
Mi Zhou ◽  
...  
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Load Forecasting ◽  
Short Term ◽  
Power Load ◽  
Power Load Forecasting ◽  
Similar Day

scholarly journals A Short-Term Residential Load Forecasting Model Based on LSTM Recurrent Neural Network Considering Weather Features

Energies ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2737
Author(s):  
Yizhen Wang ◽  
Ningqing Zhang ◽  
Xiong Chen
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Large Scale ◽  
Load Forecasting ◽  
Consumption Pattern ◽  
Forecasting Model ◽  
Short Term ◽  
Model Based ◽  
Power Load ◽  
Power Load Forecasting

With economic growth, the demand for power systems is increasingly large. Short-term load forecasting (STLF) becomes an indispensable factor to enhance the application of a smart grid (SG). Other than forecasting aggregated residential loads in a large scale, it is still an urgent problem to improve the accuracy of power load forecasting for individual energy users due to high volatility and uncertainty. However, as an important variable that affects the power consumption pattern, the influence of weather factors on residential load prediction is rarely studied. In this paper, we review the related research of power load forecasting and introduce a short-term residential load forecasting model based on a long short-term memory (LSTM) recurrent neural network with weather features as an input.

A Short-Term Power Load Forecasting Method Based on BP Neural Network

2014 ◽  
Vol 494-495 ◽  
pp. 1647-1650 ◽  
Author(s):  
Ling Juan Li ◽  
Wen Huang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
High Speed ◽  
Load Forecasting ◽  
High Accuracy ◽  
Back Propagation Algorithm ◽  
Short Term ◽  
Power Load ◽  
Forecasting Method ◽  
Power Load Forecasting

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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