scholarly journals A New Strategy for Short-Term Load Forecasting

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Yi Yang ◽  
Jie Wu ◽  
Yanhua Chen ◽  
Caihong Li
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Arima Model ◽  
Demand Forecasting ◽  
Load Forecasting ◽  
Short Term ◽  
Load Demand ◽  
Short Term Load Forecasting ◽  
New Strategy ◽  
Residual Series

Electricity is a special energy which is hard to store, so the electricity demand forecasting remains an important problem. Accurate short-term load forecasting (STLF) plays a vital role in power systems because it is the essential part of power system planning and operation, and it is also fundamental in many applications. Considering that an individual forecasting model usually cannot work very well for STLF, a hybrid model based on the seasonal ARIMA model and BP neural network is presented in this paper to improve the forecasting accuracy. Firstly the seasonal ARIMA model is adopted to forecast the electric load demand day ahead; then, by using the residual load demand series obtained in this forecasting process as the original series, the follow-up residual series is forecasted by BP neural network; finally, by summing up the forecasted residual series and the forecasted load demand series got by seasonal ARIMA model, the final load demand forecasting series is obtained. Case studies show that the new strategy is quite useful to improve the accuracy of STLF.

A comparative study of short-term load forecasting methods in distribution network

2021 ◽  
Vol 15 (1) ◽  
pp. 23-35
Author(s):  
Tuan Ho Le ◽  
◽  
Quang Hung Le ◽  
Thanh Hoang Phan
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Artificial Neural Network ◽  
Arima Model ◽  
Distribution Network ◽  
Load Forecasting ◽  
Short Term ◽  
Load Demand ◽  
Forecasting Methods ◽  
Short Term Load Forecasting

Short-term load forecasting plays an important role in building operation strategies and ensuring reliability of any electric power system. Generally, short-term load forecasting methods can be classified into three main categories: statistical approaches, artificial intelligence based-approaches and hybrid approaches. Each method has its own advantages and shortcomings. Therefore, the primary objective of this paper is to investigate the effectiveness of ARIMA model (e.g., statistical method) and artificial neural network (e.g., artificial intelligence based-method) in short-term load forecasting of distribution network. Firstly, the short-term load demand of Quy Nhon distribution network and short-term load demand of Phu Cat distribution network are analyzed. Secondly, the ARIMA model is applied to predict the load demand of two distribution networks. Thirdly, the artificial neural network is utilized to estimate the load demand of these networks. Finally, the estimated results from two applied methods are conducted for comparative purposes.

A Method Based on Wavelet Neural Network for Power System Short Term Load Forecasting

2014 ◽  
Vol 521 ◽  
pp. 303-306 ◽  
Author(s):  
Hong Mei Zhong ◽  
Jie Liu ◽  
Qi Fang Chen ◽  
Nian Liu
Keyword(s):  
Neural Network ◽  
Power System ◽  
Bp Neural Network ◽  
Load Forecasting ◽  
Wavelet Neural Network ◽  
Signal Spectrum ◽  
Short Term ◽  
Short Term Load Forecasting ◽  
Load Sequence ◽  
Daily Load

The short-term load of Power System is uncertain and the daily-load signal spectrum is continuous. The approach of Wavelet Neural Network (WNN) is proposed by combing the wavelet transform (WT) and neural network. By the WT, the time-based short-term load sequence can be decomposed into different scales sequences, which is used to training the BP neural network. The short-term load is forecasted by the trained BP neural network. Select the load of a random day in Lianyungang to study, according to the numerical simulation results, the method proves to achieve good performances.

Short-Term Load Forecasting Based on GA-Elman Model

2014 ◽  
Vol 986-987 ◽  
pp. 520-523
Author(s):  
Wen Xia You ◽  
Jun Xiao Chang ◽  
Zi Heng Zhou ◽  
Ji Lu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Relative Error ◽  
Bp Neural Network ◽  
Load Forecasting ◽  
Short Term ◽  
Elman Neural Network ◽  
Learning Efficiency ◽  
Multiple Layer ◽  
Short Term Load Forecasting

Elman Neural Network is a typical neural-network which shares the characteristics of multiple-layer and dynamic recurrent, and it’s more suitable than BP Neural Network when it’s applied to forecast the short-term load with periodicity and similarity. To solve the problem that Elman Neural Network lacks learning efficiency, GA-Elman model is established by optimizing the weights and thresholds using Genetic Algorithm. An example is then given to prove the effectiveness of GA-Elman model, using the load data of a certain region. Relative error and MSE have been considered as criterions to analyze the results of load forecasting. By comparing the results calculated by BP, Elman and GA-Elman model, the effectiveness of GA-Elman model is verified, which will improve the accuracy of short-term load forecasting.

scholarly journals Short-Term Load Forecasting of Power System Based on Improved BP Neural Network

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Power System ◽  
Bp Neural Network ◽  
Normal Load ◽  
Load Forecasting ◽  
System Optimization ◽  
Forecasting Model ◽  
Short Term ◽  
Short Term Load Forecasting

Power system load is a stochastic and non-stationary process. Due to the influence of various factors, some bad data may exist in the load observation value. These data are mixed into the normal load data to participate in the training of neural network, which seriously affects the accuracy of load forecasting. Short-term load forecasting is the basis of power system operation and analysis, improving the precision of load forecasting is an important means to ensure the scientific decision-making of power system optimization. In order to improve the precision of short term load forecasting in power system, a short-term load forecasting model based on genetic algorithm is proposed to optimize BP neural network. Firstly, using genetic algorithm to optimize the initial weights and thresholds of BP neural network to improve the prediction accuracy of BP neural network; Through the comparison and analysis before and after the model optimization, the experimental results with smaller prediction error were obtained. The simulation results show that the short-term load forecasting model established by this method has faster convergence rate and higher prediction precision.

scholarly journals Short-Term Load Forecasting Method based on Empirical Wavelet Decomposition and BLSTM Neural Networks

2021 ◽  
Author(s):  
Xiao-Yu Zhang ◽  
Stefanie Kuenzel ◽  
Nicolo Colombo ◽  
Chris Watkins
Keyword(s):  
Neural Network ◽  
Frequency Domain ◽  
Time Domain ◽  
Deep Neural Network ◽  
Load Forecasting ◽  
Short Term ◽  
Forecasting Models ◽  
Load Demand ◽  
Proposed Model ◽  
Short Term Load Forecasting

Accurate short-term load forecasting is essential to the modern power system and smart grids; the utility can better implement demand-side management and operate the power system stable with a reliable forecasting system. The load demand contains a variety of different load components, and different loads operate with different frequencies. Conventional load forecasting models (linear regression (LR), Auto-Regressive Integrated Moving Average (ARIMA), deep neural network, etc.) ignore frequency domain and can only use time-domain load demand as inputs. To make full use of both time domain and frequency domain features of the load demand, a hybrid component decomposition and deep neural network load forecasting model is proposed in this paper. The proposed model first filters noises via wavelet-based denoising technique, then decomposes the original load demand into several sublayers to show the frequency features while the time domain information is preserved as well. Then bidirectional LSTM model is trained for each sub-layer independently. To better tunning the hyperparameters, a Bayesian hyperparameter optimization algorithm is adopted in this paper. Three case studies are designed to evaluate the performance of the proposed model. From the results, it is found that the proposed model improves RMSE by 66.59% and 84.06%, comparing to other load forecasting models.<br>

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