A Robust Electricity Demand Forecasting of Rajshahi Metropolitan City of Bangladesh

Analysis of electricity demand serves as a foundation to understand the trend of demand and related variability which helps to plan for the distribution of electricity to a region. Fuzzy linear regression uses fuzzy parameters to represent ambiguous and imprecise relationships between dependent and independent variables which omits the limitations of conventional linear model. In this paper, robust optimization has been used to increase the feasibility while working with uncertainty in input data. A combined Interval-Ellipsoidal Robust Counterpart of the fuzzy linear regression model is used to analyze the data. To formulate the Robust Fuzzy Linear Regression model (RFLR), the quantity of customers and the average yearly temperature have been regarded as independent variables, while the consumption of electricity has been regarded as a dependent variable. The proposed method has been applied to forecast electricity demand of Rajshahi City. The optimal solution of the linear model has been obtained using the Lingo software. The results show the capability of the RFLR to analyze data uncertainty with more accuracy and it is demonstrated that the inaccuracy in forecasting and estimated fuzzy bands grow as the data perturbation increases.