Short-term load forecasting in smart meters with sliding window-based ARIMA algorithms

  • Alberg D
  • Last M
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Abstract

Forecasting of electricity consumption for residential and industrial customers is an important task providing intelligence to the smart grid. Accurate forecasting should allow a utility provider to plan the resources as well as to take control actions to balance the supply and the demand of electricity. This paper presents two non-seasonal and two seasonal sliding window-based ARIMA (auto regressive integrated moving average) algorithms. These algorithms are developed for short-term forecasting of hourly electricity load at the district meter level. The algorithms integrate non-seasonal and seasonal ARIMA models with the OLIN (online information network) methodology. To evaluate our approach, we use a real hourly consumption data stream recorded by six smart meters during a 16-month period.

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APA

Alberg, D., & Last, M. (2018). Short-term load forecasting in smart meters with sliding window-based ARIMA algorithms. Vietnam Journal of Computer Science, 5(3–4), 241–249. https://doi.org/10.1007/s40595-018-0119-7

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