Predictive Analysis of Energy Consumption and Electricity Demand Using Machine Learning Techniques

For efficient energy management, accurate demand forecasting is essential, necessitating the use of machine learning algorithms and careful time series analysis. Accurate demand forecasting enables utilities to make informed generating and transmission decisions, which can aid in optimising overall system performance. The dataset covers hourly energy consumption as well as other temporal variables such as day of week, month, and year. Using machine learning algorithms, we addressed the issues of anticipating energy demand. We compared the performance of three well-known gradient boosting algorithms, XGBoost, CatBoost, and LightGBM. MSE, RMSE, R-squared and MAE are among our evaluation measures. To acquire insights into the energy demand data, we also investigated several time series analysis techniques such as autocorrelation plots, spectrograms, wavelet transforms, cross-correlation plots, and phase space plots.