Short-term load forecasting using artificial neural network and linear regression across multi-city smart grid dataset

Abstract

Smart grid activities demand a remedy to stability and economic problems and Short-term load forecasting (STLF) can provide some of the basic methods to deal with them. The traditional forecasting models that are found in literature flounder about localised and region specific data sets and therefore, their acceptability of results in general is always a demanding task without their strict cross validation. This current paper develops a distinctive solution with a multi city dataset that is extensive to determine the predictive quality of Artificial Neural Networks (ANN) and Linear Regression (LR) models. The test is carried out based on the high-resolution data on hourly data on NASA Earth data platform (n = 48,048) on January 2015 to June 2020, which includes national level data on electricity demand in Panama on the National Dispatch Center (CND) and local weather data. The proposed analytical framework has incorporated two-meter elevation variables such as temperature, humidity, wind speed and precipitation in three strategic region hubs of United States viz. Tocomen, Santiago and David. The model also takes into account exogenous temporal features, including the public holidays and the academic calendars to test the change in the socio-economic loads. The experimental findings indicate that Linear Regression (LR) model is better than the Artificial Neural Network (ANN) model because it has a small Root Mean Square Error (RMSE). This means that the underlying data set is more linear in nature and in this case, simpler models can be more effective than the more complex nonlinear models, where Linear Regression performs better than ANN in the current circumstances of the data set.