A fast algorithm for short term electric load forecasting by a hidden semi-markov process

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.

KEYWORDS:

• Load forecasting
• empirical probability function
• ECM algorithm
• change point
• hidden Markov models

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The authors would like to acknowledge the funding of the National Natural Science Foundation of China [grant number 70971109], the Scientific Research Foundation of Northwest University [grant number 338050043], and Natural Science Foundation of Shaanxi Provincial Department of Education [grant number 18JK0789].