Book ChapterDOI
Short term load forecasting using neural network with rough set
Zhi Xiao,Shi-Jie Ye,Bo Zhong,Cai-Xin Sun +3 more
- pp 1259-1268
TLDR
Through attribution reduction based on variable precision with rough set, the influence of noise data and weak interdependency data to BP is avoided so the time taken for training is decreased.Abstract:
Accurate Short term load forecasting (STLF) plays a significant role in the management of power system of countries and regions on the grounds of insufficient electric energy for increased need. This paper presents an approach of back propagation neural network with rough set (RSBP) for complicated STLF with dynamic and non-linear factors in order to develop the accuracy of predictions. Through attribution reduction based on variable precision with rough set, the influence of noise data and weak interdependency data to BP is avoided so the time taken for training is decreased. Using load time series from a practical system, we tested the accuracy of forecasting in specific days with comparison.read more
Citations
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Proceedings ArticleDOI
Genetic Programming with Rough Sets Theory for Modeling Short-term Load Forecasting
TL;DR: A novel method of genetic programming with rough sets (RS) theory is developed to model STLF to improve the accuracy and enhance the robustness of load forecasting results and is shown that the presented forecasting method is more accurate and efficient.
Journal ArticleDOI
Multi-directional gated recurrent unit and convolutional neural network for load and energy forecasting: A novel hybridization
TL;DR: In this paper , a fusion of novel multi-directional gated recurrent unit (MD-GRU) with CNN using global average pooling (GAP) as hybridization is being proposed for load and energy forecasting.
References
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Journal ArticleDOI
Forecasting regional electricity load based on recurrent support vector machines with genetic algorithms
Ping-Feng Pai,Wei-Chiang Hong +1 more
TL;DR: In this article, a recurrent support vector machines with genetic algorithms (RSVMG) is proposed to forecast electricity load, which is a promising alternative for forecasting electricity load in power industry.
Posted Content
Support Vector Machines with Simulated Annealing Algorithms in Electricity Load Forecasting
Wei-Chiang Hong,Wei-Chiang Hong +1 more
TL;DR: In this article, a support vector machine with simulated annealing (SVMSA) model was proposed to forecast the electricity load in Taiwan, which outperformed the other two models, namely the autoregressive integrated moving average (ARIMA) model and the general regression neural networks (GRNN) model.
Journal ArticleDOI
Support vector machines with simulated annealing algorithms in electricity load forecasting
Ping-Feng Pai,Wei-Chiang Hong +1 more
TL;DR: In this paper, a support vector machine with simulated annealing (SVMSA) model was proposed to forecast the electricity load in Taiwan, which outperformed the other two models, namely the autoregressive integrated moving average (ARIMA) model and the general regression neural networks (GRNN) model.
Journal ArticleDOI
An adaptive neural-wavelet model for short term load forecasting
Bailing Zhang,Zhao Yang Dong +1 more
TL;DR: In this article, the authors proposed a model for short term load forecast in the competitive electricity market based on wavelet multi-resolution decomposition by autocorrelation shell representation and neural networks (multilayer perceptrons or MLPs) modeling of wavelet coefficients.
Journal ArticleDOI
Wavelet transform and neural networks for short-term electrical load forecasting
TL;DR: In this paper, a novel approach is proposed for short-term electrical load forecasting by combining the wavelet transform and neural networks, which decomposes the historical load into an approximate part associated with low frequencies and several detail parts associated with high frequencies.