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Load forecasting using mutual information, adaptive FNN and Artificial Bee search model
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Abstract: (14132 Views) |
| Accurate load forecasting becomes more and more important for all market participants in competitive electricity markets, which can maximize producers’ profits and consumers’ utilities, respectively. The optimal profit is determined by applying a perfect price forecast. A load forecast with a less prediction errors, yields maximum profits for market players. The numerical electricity load forecasting is high in forecasting errors of various approaches. However, electricity load is a complex signal due to its nonlinearity, non-stationary, and time variant behavior. In spite of much research in this area, more accurate and robust price forecast methods are still required. In this paper, a new hybrid forecast technique based on feature selection technique, Artificial Neural Network (ANN) and Artificial Bee Colony (ABC) model is proposed for load forecasting. The feature selection method is an improved version of the mutual information (MI) technique. The superiority of this proposed method is examined by using the data acquired from New England market. Empirical results show that this proposed method performs better than some of the other price forecast techniques. |
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| Keywords: load forecasting, neural network, Electricity load classification, Electricity market, mutual information (MI). |
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Full-Text [PDF 490 kb]
(6264 Downloads)
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Type of Study: Research |
Subject:
Special
Received: 2012/11/30 | Accepted: 2013/09/21 | Published: 2013/09/21
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