Electricity Consumption - Monitoring and Forecasting

Accurate forecasting and monitoring electricity consumption has become very important for efficient energy management as well as grid optimization and strategic resource management. The following study aims to evaluate various machine learning models which included - Random Forest, XGBoost, Neural Networks along with a combined ensemble approach to enhance predictive accuracy, moreover it explores the impact of feature engineering and hyperparameter tuning in optimizing model performance. A synthetic dataset which spanned a duration of three years was utilized for the study. The dataset consisted of more than twenty-six thousand hourly records and incorporated temporal, environmental and historical consumption features. Data preprocessing involved standardization for neural networks and feature selection to optimize predictive performance. Among the models tested, XGBoost exhibited the highest accuracy, achieving a Mean Absolute Error (MAE) of 0.20 and an R2 score of 0.59, outperforming the baseline and other approaches. The Ensemble model integrated Random Forest, XGBoost, and Neural Networks demonstrated robust performance and yielded stable predictions. Feature selection and importance analysis identified temperature and historical consumption as the most influential factors. Further, future improvements like leveraging advanced deep learning architecture such as LSTMs and introducing real-time IoT data can enhance the predictive capabilities and accuracy.