A study of marco factors on electricity load demand in Johor Bahru using statistical approach - Mohammad Hanif Jifri (2018)
As widely known, load demand forecasting plays a vital role in power system planning and management in meeting the load demand requirements particularly during the peak demand period. There are many macro factors currently being identified to have influence over the load demand pattern which includes the population, economy and meteorological factors. However, there are problems pertaining to these factors caused by limited availability of statistical analysis to analyse which factors give the most contributing effect load demand in Johor Bahru. For that reason, three impo1iant objectives were defined based on the through literature found by earlier related researches. Besides that, data for electricity consumption were provided by Tenaga Nasional Berhad (TNB). For all others significant macro factors, the data provided are the actual data specifically for Johor Bahru during the year of 2005 until 201 1 . Initially, an investigation was done to identify which macro factors that will have an effect on the load demand prediction by using Pearson Correlation coefficient. Since only a few mathematical analysis, traditional forecasting technique done previously focused on how to determine the relationship between these macro factors and electricity load, thus this research proposed to explore further the three mathematical models namely regression, time series and hybrid methods. Using these three mathematical models, this present research provides an electricity demand estimation and forecast, whilst comparing the results with official projections. Therefore, the next goal is to find the most influential macro factor which can help improves the accuracy of the medium-term load forecasting. The performance of these different methods were evaluated by using the forecasting accuracy criteria namely, Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). As a result, this research found that maximum temperature, population growth and Gross Domestic Product (GDP) have influenced in determining the electricity demand consumption. In addition, the Multiple Stepwise Regression method was identified as the best forecasting method based on the smallest RMSE and MAPE obtained specifically for the city of Johor Bahru load demand prediction. In terms of contribution, it is expected that the mathematical models will help electricity demand planners to accurately plan load demand for future consumption in Johor Bahru area.