Abstract
Price forecasting acts as an essential position in the current energy industry as to assist the independent generators in putting on a remarkable bidding system and scheming contracts, and helps with the selection of supply on the advance generation facility in the long term. These electricity prices are usually hard to predict as it always depends on the uncertainty factors which results in severe volatility or even spikes of price in the energy market. Therefore, determining the accuracy of electricity price forecasting had become an even more important task as there are often remains some crucial prices volatility in the electric power market. This approach focuses on the parameter selection (hidden neuron, learning rate, and momentum rate) and the selection of input data for three types of model. By using the appropriate parameters and inputs, the accuracy of the prediction can be improved. This approach is expected to provide market participants a better bidding strategy and will be used to boost profits in the energy markets using the artificial neural network.
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Acknowledgements
This study is supported in part by the Fundamental Research Grant Scheme (FRGS) provided by the Ministry of Higher Education Malaysia (FRGS/1/2017/TK04/FKE-CERIA/F00331). We also would like to dedicate our appreciation to Universiti Teknikal Malaysia Melaka (UTeM) for providing technical and moral support throughout conducting this study.
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Ibrahim, N.N.A.N., Razak, I.A.W.A., Sidin, S.S.M., Bohari, Z.H. (2019). Electricity Price Forecasting Using Neural Network with Parameter Selection. In: Piuri, V., Balas, V., Borah, S., Syed Ahmad, S. (eds) Intelligent and Interactive Computing. Lecture Notes in Networks and Systems, vol 67. Springer, Singapore. https://doi.org/10.1007/978-981-13-6031-2_33
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DOI: https://doi.org/10.1007/978-981-13-6031-2_33
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