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Forecasting Monthly Electricity Demands: An Application of Neural Networks Trained by Heuristic Algorithms

1
Department of Industrial Engineering and Systems Management, Feng Chia University, Taichung 40724, Taiwan
2
Faculty of Information Technology, University of Transport Technology, Hanoi 100000, Vietnam
*
Author to whom correspondence should be addressed.
Academic Editor: Willy Susilo
Information 2017, 8(1), 31; https://doi.org/10.3390/info8010031
Received: 26 December 2016 / Revised: 6 March 2017 / Accepted: 7 March 2017 / Published: 10 March 2017
Electricity demand forecasting plays an important role in capacity planning, scheduling, and the operation of power systems. Reliable and accurate prediction of electricity demands is therefore vital. In this study, artificial neural networks (ANNs) trained by different heuristic algorithms, including Gravitational Search Algorithm (GSA) and Cuckoo Optimization Algorithm (COA), are utilized to estimate monthly electricity demands. The empirical data used in this study are the historical data affecting electricity demand, including rainy time, temperature, humidity, wind speed, etc. The proposed models are applied to Hanoi, Vietnam. Based on the performance indices calculated, the constructed models show high forecasting performances. The obtained results also compare with those of several well-known methods. Our study indicates that the ANN-COA model outperforms the others and provides more accurate forecasting than traditional methods. View Full-Text
Keywords: Cuckoo Optimization Algorithm; Gravitational Search Algorithm; neural networks; forecasting; monthly electricity demand Cuckoo Optimization Algorithm; Gravitational Search Algorithm; neural networks; forecasting; monthly electricity demand
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MDPI and ACS Style

Chen, J.-F.; Lo, S.-K.; Do, Q.H. Forecasting Monthly Electricity Demands: An Application of Neural Networks Trained by Heuristic Algorithms. Information 2017, 8, 31.

AMA Style

Chen J-F, Lo S-K, Do QH. Forecasting Monthly Electricity Demands: An Application of Neural Networks Trained by Heuristic Algorithms. Information. 2017; 8(1):31.

Chicago/Turabian Style

Chen, Jeng-Fung; Lo, Shih-Kuei; Do, Quang H. 2017. "Forecasting Monthly Electricity Demands: An Application of Neural Networks Trained by Heuristic Algorithms" Information 8, no. 1: 31.

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