SHORT-TERM LOAD FORECASTING USING MILTI LINER REGRESSION BASED ON ARTIFICIAL NEURAL NETWORK
Journal Title: Молодий вчений - Year 2018, Vol 2, Issue 54
Abstract
Load forecasting is a important tool for the energy industry, as it can influence areas like power generation and trading, infrastructure planning, etc. The implementation of the load forecasting tool in distribution networks has a wider impact up to the level of electricity generation. Load forecasting has been an area of energy systems, where human experts still perform better than the algorithms which were put forward as alternatives. Many techniques have been put forward for the accurate load forecasting. Different Artificial Neural Networks (ANN) with different architectures have been proposed in the last few years for load forecasting purpose resulting in a large number of publications on this subject. In this paper, we propose a New Neural network direct power supply for short-term load forecasting depending on the weather changes for systems of distribution management. The proposed neural network can predict the load profile with a run time of one to seven days.
Authors and Affiliations
O. I. Boiko, Sheng Hong
Keywords
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