Forecasting next-hour electricity demand in small-scale territories: Evidence from Jordan

Abstract

In exceptional times of wars, natural crises (e.g., snow storms), or hosting massive events (e.g., international sports events), prior knowledge of hour-by-hour electricity demand might become critical for the concerned areas. Anticipating the next-hour demand within a bounded location might help cope with challenging situations. In this paper, we are concerned with the problem of electricity demand forecasting for the next hour within a small area in the context of the country of Jordan relying exclusively on historical electricity consumption. Existing electricity demand forecasting models predict electricity demand for the next day for the whole country of Jordan or for large cities using numerous indicators such as weather conditions and season-related features, together with historical electricity consumption. Our proposed solution consists of preprocessing the hourly-electricity-consumption data to rearrange it into time series of 25-hour length. Then, we applied supervised machine learning algorithms to build predictive models for electricity demand. Our methods proved effective to a certain degree in forecasting electricity demand using time series of historical electricity consumptions only. We employ several predictive machine learning models: neural network, decision tree, random forest, AdaBoost, support vector regression, nearest neighbors, linear regression, bayesian ridge, partial least squares, gradient boosting, and principal component regression. We found that the most critical indicators in our predictive models are the electricity consumption of the two hours preceding the target hour and the electricity consumption the day before in the hour (the same as the target hour) and the next hour. Our predictive models can efficiently forecast electricity demand in a small rural area in Jordan with an accuracy of nearly 97% at best and 92% at least. Our predictive models are constructed using historical data that includes the electricity consumption of a small Jordanian district for the years 2019–2021.