scholarly journals Short-term Load Prediction of Integrated Energy System with Wavelet Neural Network Model Based on Improved Particle Swarm Optimization and Chaos Optimization Algorithm

2021 ◽  
Vol 9 (6) ◽  
pp. 1490-1499
Author(s):  
Leijiao Ge ◽  
Yuanliang Li ◽  
Jun Yan ◽  
Yuqian Wang ◽  
Na Zhang
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Neural Network Model ◽  
Energy System ◽  
Load Prediction ◽  
Short Term ◽  
Swarm Optimization ◽  
Chaos Optimization ◽  
Chaos Optimization Algorithm ◽  
Integrated Energy System

scholarly journals Short-term load prediction of integrated energy system based on neural network

2021 ◽  
Vol 2087 (1) ◽  
pp. 012016
Author(s):  
Yao Wang ◽  
Xuxia Li ◽  
Yan Liang ◽  
Yingying Hu ◽  
Xiaoming Zheng ◽  
...  
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Short Term Memory ◽  
Energy System ◽  
Load Prediction ◽  
Short Term ◽  
Nonlinear Characteristics ◽  
Integrated Energy System ◽  
Long Short Term Memory ◽  
Grey Relation

Abstract Considering the correlation and nonlinear characteristics of multiple types of loads in the integrated energy system, grey relation analysis (GRA) and long short term Memory (LSTM) neural network are selected to establish the short-term load prediction model of the integrated energy system. The model uses GRA method to analyze the coupling between multiple types of loads and the meteorological factors, and then obtains the load forecast results through the LSTM prediction model. Finally, a practical example is given to verify the validity of the model.

An improved self-organizing incremental neural network model for short-term time-series load prediction

2021 ◽  
Vol 292 ◽  
pp. 116912
Author(s):  
Rong Wang Ng ◽  
Kasim Mumtaj Begam ◽  
Rajprasad Kumar Rajkumar ◽  
Yee Wan Wong ◽  
Lee Wai Chong
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Network Model ◽  
Neural Network Model ◽  
Load Prediction ◽  
Short Term ◽  
Self Organizing

scholarly journals A short term load forecasting of integrated energy system based on CNN-LSTM

2020 ◽  
Vol 185 ◽  
pp. 01032
Author(s):  
Xianjun Qi ◽  
Xiwei Zheng ◽  
Qinghui Chen
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Pearson Correlation ◽  
Load Forecasting ◽  
Energy System ◽  
Composite Model ◽  
Short Term ◽  
Characteristic Quantity ◽  
Integrated Energy System ◽  
Coupling Characteristics

The accurate forecast of integrated energy loads, which has important practical significance, is the premise of the design, operation, scheduling and management of integrated energy systems. In order to make full use of the coupling characteristics of electricity, cooling and heating loads which is difficult to deal with by traditional methods, this paper proposes a new forecast model of integrated energy system loads based on the combination of convolutional neural network (CNN) and long short term memory (LSTM). Firstly, the Pearson correlation coefficients among the electricity, cooling and heating load series of the integrated energy system are calculated, and the results show that there is a strong coupling relationship between the loads of an integrated energy system. Then, the CNN-LSTM composite model is constructed, and CNN is used to extract the characteristic quantity which reflects the load coupling characteristics of the integrated energy system. Then, the characteristic quantity is converted into the time series input to LSTM, and the excellent time series processing ability of LSTM is used for load forecasting. The results show that the CNN-LSTM composite model proposed in this paper has higher prediction accuracy than the wavelet neural network model, CNN model and LSTM model.

scholarly journals Models for Short-Term Wind Power Forecasting Based on Improved Artificial Neural Network Using Particle Swarm Optimization and Genetic Algorithms

Energies ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 2873 ◽  
Author(s):  
Dinh Thanh Viet ◽  
Vo Van Phuong ◽  
Minh Quan Duong ◽  
Quoc Tuan Tran
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Wind Power ◽  
Optimization Algorithm ◽  
Electricity Market ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Short Term ◽  
Swarm Optimization

As sources of conventional energy are alarmingly being depleted, leveraging renewable energy sources, especially wind power, has been increasingly important in the electricity market to meet growing global demands for energy. However, the uncertainty in weather factors can cause large errors in wind power forecasts, raising the cost of power reservation in the power system and significantly impacting ancillary services in the electricity market. In pursuance of a higher accuracy level in wind power forecasting, this paper proposes a double-optimization approach to developing a tool for forecasting wind power generation output in the short term, using two novel models that combine an artificial neural network with the particle swarm optimization algorithm and genetic algorithm. In these models, a first particle swarm optimization algorithm is used to adjust the neural network parameters to improve accuracy. Next, the genetic algorithm or another particle swarm optimization is applied to adjust the parameters of the first particle swarm optimization algorithm to enhance the accuracy of the forecasting results. The models were tested with actual data collected from the Tuy Phong wind power plant in Binh Thuan Province, Vietnam. The testing showed improved accuracy and that this model can be widely implemented at other wind farms.

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