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>

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>

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.

Analysis and evaluation of two short-term load forecasting techniques

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Saroj Kumar Panda ◽  
Papia Ray
Keyword(s):  
Neural Network ◽  
Power System ◽  
Deep Neural Network ◽  
Load Forecasting ◽  
Percentage Error ◽  
Correlation Technique ◽  
Short Term ◽  
Efficient Operation ◽  
Decision Mechanism ◽  
Short Term Load Forecasting

Abstract Short-term load forecasting (STLF) is very important for an efficient operation of the power system because the exact and stable load forecasting brings good results to the power system. This manuscript presents the application of two new models in STLF i.e. Cross multi-models and second decision mechanism and Residential load forecasting in smart grid using deep neural network models. In the cross multi-model and second decision mechanism method, the horizontal and longitudinal load characteristics are useful for the construction of the model with the calculation of the total load. The dataset for this model is considered from Maine in New England, Singapore, and New South Wales of Australia. While, In the residential load forecasting method, the Spatio-temporal correlation technique is used for the construction of the iterative ResBlock and deep neural network which helps to give the characteristics of residential load with the use of a publicly available Redd dataset. The performances of the proposed models are calculated by the Root Mean Square Error, Mean Absolute Error, and Mean Absolute Percentage Error. From the simulation results, it is concluded that the performance of cross multi-model and second decision mechanism is good as compare to the residential load forecasting.

scholarly journals Short Term Load Forecasting of Distribution Feeder Using Artificial Neural Network Technique

2021 ◽  
Vol 2 (1) ◽  
pp. 1-22
Author(s):  
Kumilachew Chane ◽  
◽  
Fsaha Mebrahtu Gebru ◽  
Baseem Khan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Load Forecasting ◽  
Electric Utility ◽  
Percentage Error ◽  
Electrical Load ◽  
Short Term ◽  
Load Demand ◽  
Short Term Load Forecasting ◽  
Artificial Neural

This paper explains the load forecasting technique for prediction of electrical load at Hawassa city. In a deregulated market it is much need for a generating company to know about the market load demand for generating near to accurate power. If the generation is not sufficient to fulfill the demand, there would be problem of irregular supply and in case of excess generation the generating company will have to bear the loss. Neural network techniques have been recently suggested for short-term load forecasting by a large number of researchers. Several models were developed and tested on the real load data of a Finnish electric utility at Hawassa city. The authors carried out short-term load forecasting for Hawassa city using ANN (Artificial Neural Network) technique ANN was implemented on MATLAB and ETAP. Hourly load means the hourly power consumption in Hawassa city. Error was calculated as MAPE (Mean Absolute Percentage Error) and with error of about 1.5296% this paper was successfully carried out. This paper can be implemented by any intensive power consuming town for predicting the future load and would prove to be very useful tool while sanctioning the load.

scholarly journals Short-Term Load Forecasting Using Adaptive Annealing Learning Algorithm Based Reinforcement Neural Network

2016 ◽  
Author(s):  
Cheng-Ming Lee ◽  
Chia-Nan Ko
Keyword(s):  
Neural Network ◽  
Learning Algorithm ◽  
Load Forecasting ◽  
Support Vector ◽  
Initial Structure ◽  
Short Term ◽  
Power Company ◽  
Learning Rates ◽  
Proposed Model ◽  
Short Term Load Forecasting

A reinforcement learning algorithm is proposed to improve the accuracy of short-term load forecasting (STLF) in this article. The proposed model integrates radial basis function neural network (RBFNN), support vector regression (SVR), and adaptive annealing learning algorithm (AALA). In the proposed methodology, firstly, the initial structure of RBFNN is determined by using SVR. Then, an AALA with time-varying learning rates is used to optimize the initial parameters of SVR-RBFNN (AALA-SVR-RBFNN). In order to overcome the stagnation for searching optimal RBFNN, a particle swarm optimization (PSO) is applied to simultaneously find promising learning rates in AALA. Finally, the short-term load demands are predicted by using the optimal RBFNN. The performance of the proposed methodology is verified on the actual load dataset from Taiwan Power Company (TPC). Simulation results reveal that the proposed AALA-SVR-RBFNN can achieve a better load forecasting precision as compared to various RBFNNs.

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.

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