Short-term Electricity Load Forecasting for Building Energy Management System

Authors

  • Phanumat Saatwong
  • Surapong Suwankawin

Keywords:

Short-term Electricity Load Forecasting;, Box-Jenkins Methodology;, Seasonal Autoregressive Integrated Moving Average Model;, Building Energy Management System.

Abstract

Short-term electricity load forecasting is essential for Building Energy Management System (BEMS) in various aspects, e.g. peak-shaving application, planning for self-consumption with renewable energy, net-zero energy building.  This paper presents the forecast models for load demand in buildings by using the time-series approach. The load-forecast models are created from the step-by-step procedure of Box-Jenkins Methodology and the Seasonal Autoregressive Integrated Moving Average (SARIMA) models are obtained. The resultant models are evaluated with the actual load of Electrical Engineering Building at Chulalongkorn University. The proposed models can fairly forecast the load pattern for the workdays with roughly 20% Mean Absolute Percentage Error (MAPE). In addition, the models are moderately successful to predict the peak-load instant.

References

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Published

2020-06-19

How to Cite

[1]
P. . . Saatwong and S. . Suwankawin, “Short-term Electricity Load Forecasting for Building Energy Management System”, Eng. & Technol. Horiz., vol. 33, no. 2, pp. 16–21, Jun. 2020.

Issue

Vol. 33 No. 2 (2016): april -june

Section

Research Articles

License

The published articles are copyrighted by the School of Engineering, King Mongkut's Institute of Technology Ladkrabang.

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