Volume 55

Assessing the Impact of Climate Change on Long Term Load Forecasting for Electric Utilities Nadia Shams, Aleena Ahmad, Naveed Arshad

Abstract

This paper presents a comparison of different statistical and machine learning models using five groups of input features to forecast the future demand of a region, comprising 5 adjacent cities (Lahore, Sheikhupura, Kasur, Okara, Nankana), managed by a single government-operated utility. The proposed methodology was constructed using hourly data of electricity load in MW and hourly data of three weather variables with the highest correlation with load, for more than 10 years i.e. from July 2013 to Dec 2023. We have applied LM, SVM, and LSTM models, the most widely used techniques in literature for long term load forecasts. In three years of out-of-sample forecasts with 26041 timesteps, the SVM model performed best with a MAPE value of 2.98% applying ARIMA with Kalman smoothing to fill missing load values for 4 months. However, LSTM performed 2.5 times better than LM and SVM if input features are only weather variables and historical hourly load is not provided as input to training and test sets.

Keywords Power System planning, Load Forecasting, Climate change