Machine Learning Based Cost Effective Electricity Load Forecasting Model Using Correlated Meteorological Parameters

Electricity, a fundamental commodity, must be generated as per required utilization which cannot be stored at large scales. The production cost heavily depends upon the source such as hydroelectric power plants, petroleum products, nuclear and wind energy. Besides overproduction and underproduction, electricity demand is driven by metrological parameters, economic and industrial activities. Therefore, the region specific accurate electric load forecasting can help to effectively manage, plan, and schedule appropriate low cost electricity generation units to decrease per unit cost and provision of on time energy for maximum financial benefits. Machine learning (ML) offers different supervised learning algorithms including multiple linear regression, support vector regressors with different kernels, k-nearest neighbors, Random Forest and AdaBoost to forecast the time series data, but the performance of these algorithms is data dependent. It is vitally important to consider correlated metrological parameters of the specific region for accurate prediction of electricity load demand using ML based forecasting models to minimize the price per unit. In this study, an algorithm is proposed to select least cost electric load forecasting model (lcELFM) using correlated meteorological parameters. We developed least cost forecasting models by minimizing root mean squared error, mean absolute error, and mean absolute percentage error. For simulations, the recorded electricity demand data is taken from a substation of water and power development authority Muzaffarabad city from 1^ {st} January 2014 to 31^ {st} December 2015. The meteorological time series data are obtained from the substation of Pakistan meteorological department for the same period and same region. Empirical results demonstrate the robustness of the proposed algorithm to select lcELFM. Moreover, SVR (Radial) based electric load forecasting model proves to be the robust model when built using correlated features (temperature and dew point) for the said region and in turn can save up to PKR 0. 313 million daily.