A fast algorithm for short term electric load forecasting by a hidden semi-markov process |
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Authors: | Qihong Duan Dengfu Zhao |
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Institution: | 1. School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, People's Republic of China;2. School of Electrical Engineering, Xi'an Jiaotong University, Xi'an, People's Republic of China |
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Abstract: | The paper proposes an alternative algorithm to implement the current manual choosing mechanism of energy companies whose dedicated department has a library of electric load models and one best model is chosen manually everyday for daily forecast. The proposed algorithm is a combination of an estimation of change point, and a fast ECM algorithm based on the empirical probability function, as well as methods of a hidden markov chain. We train parameters of the proposed algorithm based on a historical dataset consisting of loads, exogenous information such as temperature, and the daily recommended best model which is unavailable sometimes. Simulations and a test on a real-world dataset show that compared with other state-of-art algorithms, the proposed algorithm is fast and efficient for short-term electric load forecasting. An implement to the proposed algorithm written in Matlab is provided in supplement file. |
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Keywords: | Load forecasting empirical probability function ECM algorithm change point hidden Markov models |
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