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.

A NOVEL APPROACH FOR SHORT-TERM LOAD FORECASTING USING SUPPORT VECTOR MACHINES

2004 ◽  
Vol 14 (05) ◽  
pp. 329-335 ◽  
Author(s):  
LIANG TIAN ◽  
AFZEL NOORE
Keyword(s):  
Neural Network ◽  
Load Forecasting ◽  
Support Vector ◽  
Short Term ◽  
Feed Forward Neural Network ◽  
Modeling Approach ◽  
Novel Approach ◽  
Power Load ◽  
Short Term Load Forecasting ◽  
Network Approaches

A support vector machine (SVM) modeling approach for short-term load forecasting is proposed. The SVM learning scheme is applied to the power load data, forcing the network to learn the inherent internal temporal property of power load sequence. We also study the performance when other related input variables such as temperature and humidity are considered. The performance of our proposed SVM modeling approach has been tested and compared with feed-forward neural network and cosine radial basis function neural network approaches. Numerical results show that the SVM approach yields better generalization capability and lower prediction error compared to those neural network approaches.

scholarly journals Analysis of Different Neural Networks and a New Architecture for Short-Term Load Forecasting

Energies ◽  
2019 ◽  
Vol 12 (8) ◽  
pp. 1433 ◽  
Author(s):  
Lintao Yang ◽  
Honggeng Yang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Scale ◽  
Load Forecasting ◽  
Short Term ◽  
Electric System ◽  
Power Company ◽  
Network Methods ◽  
Short Term Load Forecasting ◽  
Electric Power Company

Short-term load forecasting (STLF) has been widely studied because it plays a very important role in improving the economy and security of electric system operations. Many types of neural networks have been successfully used for STLF. In most of these methods, common neural networks were used, but without a systematic comparative analysis. In this paper, we first compare the most frequently used neural networks’ performance on the load dataset from the State Grid Sichuan Electric Power Company (China). Then, considering the current neural networks’ disadvantages, we propose a new architecture called a gate-recurrent neural network (RNN) based on an RNN for STLF. By evaluating all the methods on our dataset, the results demonstrate that the performance of different neural network methods are related to the data time scale, and our proposed method is more accurate on a much shorter time scale, particularly when the time scale is smaller than 20 min.

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 Daily Peak Load Forecast Using Artificial Neural Network

2019 ◽  
Vol 9 (4) ◽  
pp. 2256
Author(s):  
Ramesh Kumar V ◽  
Pradipkumar Dixit
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Learning Algorithm ◽  
Peak Load ◽  
Load Forecasting ◽  
Percentage Error ◽  
Ann Model ◽  
Short Term ◽  
Short Term Load Forecasting ◽  
Artificial Neural

The paper presents an Artificial Neural Network (ANN) model for short-term load forecasting of daily peak load. A multi-layered feed forward neural network with Levenberg-Marquardt learning algorithm is used because of its good generalizing property and robustness in prediction. The input to the network is in terms of historical daily peak load data and corresponding daily peak temperature data. The network is trained to predict the load requirement ahead. The effectiveness of the proposed ANN approach to the short-term load forecasting problems is demonstrated by practical data from the Bangalore Electricity Supply Company Limited (BESCOM). The comparison between the proposed and the conventional methods is made in terms of percentage error and it is found that the proposed ANN model gives more accurate predictions with optimal number of neurons in the hidden layer.

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