Predicting Energy Demand: Exploring Temporal and Spatial Variations

This paper aims to create a model to predict energy demand based on temporal, meteorological, and contextual variables to enhance energy management. As there is an increase in interest in smart grids and renewable energy, demand forecasting with high accuracy is crucial to maximize resource allocation, improve energy efficiency, and maintain grid stability. The variables include hour records, time, temperature, humidity, wind speed, season, weather, levels of demand, and power generating capacity. The study delves into methods by which factors such as day of the week, time of day, and seasons influence the usage of energy, and meteorological influences such as humidity and wind speed. Using techniques such as Support Vector Machines (SVM), Decision Trees (DT), and Ensemble Voting Classifiers, it attempts to build a machine learning model categorizing energy demand into Low, Medium, or High. High-quality input data is guaranteed using preprocessing techniques, and recall, accuracy, and precision are used to evaluate the performance of models. The present research is in support of the UN Sustainable Development Goals (SDGs) in providing low-cost clean energy, city resilience, and mitigating climate change through the maximum utilization of energy and renewable integration.