scholarly journals Midterm Power Load Forecasting Model Based on Kernel Principal Component Analysis and Back Propagation Neural Network with Particle Swarm Optimization

Big Data ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 130-138 ◽  
Author(s):  
Zhao Liu ◽  
Xincheng Sun ◽  
Shuai Wang ◽  
Mengjiao Pan ◽  
Yue Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Load Forecasting ◽  
Back Propagation ◽  
Principal Component ◽  
Back Propagation Neural Network ◽  
Kernel Principal Component Analysis ◽  
Forecasting Model ◽  
Swarm Optimization ◽  
Power Load ◽  
Power Load Forecasting

scholarly journals Application of Chaos and Neural Network in Power Load Forecasting

2011 ◽  
Vol 2011 ◽  
pp. 1-12 ◽  
Author(s):  
Li Li ◽  
Liu Chong-xin
Keyword(s):  
Neural Network ◽  
Chaos Theory ◽  
Load Forecasting ◽  
Back Propagation ◽  
Shaanxi Province ◽  
System Delay ◽  
Power Load ◽  
Power Load Forecasting ◽  
Artificial Neural Network Ann ◽  
Bp Neural Network Model

This paper employs chaos theory into power load forecasting. Lyapunov exponents on chaos theory are calculated to judge whether it is a chaotic system. Delay time and embedding dimension are calculated to reconstruct the phase space and determine the structure of artificial neural network (ANN). Improved back propagation (BP) algorithm based on genetic algorithm (GA) is used to train and forecast. Finally, this paper uses the load data of Shaanxi province power grid of China to complete the short-term load forecasting. The results show that the model in this paper is more effective than classical standard BP neural network model.

scholarly journals Power Load Forecasting Model Based on Grey Neural Network Regression Combination

2020 ◽  
Vol 213 ◽  
pp. 03006
Author(s):  
Guozhen Ma ◽  
Ning Pang ◽  
Zeya Zhang ◽  
Yongli Wang ◽  
Chen Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Load Forecasting ◽  
High Accuracy ◽  
Actual Data ◽  
Forecasting Model ◽  
Combined Model ◽  
Model Method ◽  
Model Based ◽  
Power Load ◽  
Power Load Forecasting

Due to the limitations of a single power load forecasting model, the power load forecasting cannot be performed well. In order to obtain a greater closeness to predict results with actual data, this paper presents the power load forecasting model based on gray neural network combined return to Guangzhou, 2010 - 2019 on actual data for example, the results show that: As used herein, the combined model method has high accuracy and strong use value.

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.

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