Short-Term Load Forecasting using a Cluster of Neural Networks for the Greek Energy Market.
SETN(2016)
摘要
In the context of the liberalization of the Greek Energy Market, load forecasting is essential in various system programming procedures. Short-term load forecasting extends from one to seven days, although in this paper a model is proposed for the next calendar day in step of sixty minutes. The objective is to design and implement a software-based short-term load forecasting model for the Greek interconnected transmission system that will show improved performance compared to previous methods. The proposed model introduces a categorization of the forecasted days along with a dedicated artificial neural network for each category. Appropriate input vectors are selected at the training process for each custom-built network.
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关键词
Artificial Neural Networks, Short-term Load Forecasting, Greek Energy Market
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