Forecasting electricity consumption by multiple linear regression - Tay, K. G.; Choy, Y. Y.; Huong, Audrey (International Journal of Engineering and Technology, 2018)
Electricity consumption forecasting is crucial for effective operation, planning and facility expansion of the power system. An accurate forecasts can save operating and maintenance costs. As a result, increased the reliability of power supply and delivery system. Universiti Tun Hussein Onn Malaysia (UTHM) is a developing Malaysian Technical University. There is a great development of UTHM infrastructure since its formation in 1993. The development will be accompanied with the increasing demand for electricity. Hence, there is a need to forecast UTHM electricity consumption for future decisions on generating electric power, load switching, and infrastructure development. The UTHM load demand was forecasted by using multiple linear regression (MLR). The monthly load demand from January 2011 to August 2018 was used to forecast January to August 2019 monthly load demand. MLR can forecast the UTHM load demand quite well with mean absolute percentage error (MAPE) of 10.62%. MLR was then compared with curve fitting methods from an Excel spreadsheet.