Electricity Demand Forecast of College of Science and Technology, Royal University of Bhutan by 2030

Increase in the number of students and staff in the campus has led to increase in the consumption of electricity from the gird. It is important to have reliable electricity plan to meet the future needs and to become self-sufficient. This paper presents a forecast of the electricity demand of the College of Science and Technology until 2030. The historical electricity consumption data from January 2014 until December 2018 was used for the forecast. The future electricity consumption was forecasted using Autoregressive Integrated Moving Average (ARIMA) model in XLSTAT. ARIMA was specified by three order parameters (p, d, q). To identify the model of ARIMA, the autocorrelation function (ACF), and partial autocorrelation function (PACF) were used. The efficiency of the model was checked using root mean square error (RMSE), mean square error (MSE), and the sum of square error (SSE). The forecast was also validated using the best fit comparison of raw data with the predicted data. The total electricity consumption of the college is forecast to increase from 1.09 MWh in 2018 to 5.75 MWh in 2030 with an average increase of 14.67 % per year. Similarly, electricity consumption in the staff residential zone is projected to increase from 166 MWh in 2018 to 295 MWh in 2030. In the case of student residential zone, the electricity consumption is forecast to increase from 273 MWh in 2018 to 361 MWh by 2030. Keywords: Time series analysis, ARIMA model, electricity demand, demand forecasting. DOI: 10.7176/JEP/11-30-05 Publication date: October 31 st 2020