Temporal Energy Consumption Exploration and Time Series Forecasting of Electricity Dynamics

In recent times, the judicious management of energy resources has emerged as a paramount concern owing to the escalating global energy demand. Studying and predicting energy consumption patterns plays an important role for effective demand side management, providing the optimal allocation of energy resources, and promoting sustainability. This research fulfills the gap by providing a study of temporal consumption patterns. The essential aim is to use forecasting techniques for predicting household electricity usage on an hourly basis, in contrast to the actual energy production metrics from the power houses in India. The study is performed on the energy consumption dataset from city of Seaux, France, adding hourly electricity usage and readings from various residents over a time period. To deepen this understanding of the dynamics of demand and supply by the energy industry, it is complemented by parallel analysis on actual production data from Indian power stations. Against the backdrop of many intricate time-lagged relationships in the energy sector, state-of-the-art multivariate time series forecasting (MTSF), Prophet and Long Short-Term Memory (LSTM) networks, approaches have been applied and found useful in interpreting complex temporal correlations present within energy production and consumption data. The hybrid models Convolutional Neural Networks (CNN- Gated Recurrent Unit (GRU)), CNN-LSTM, Stacked GRU and Stacked LSTM, LSTM Dense, GRU Dense has been implemented. The comparative analysis is carried out to know the advantages and disadvantages of the applied forecasting models. The GRU Dense Model emerged as the most accurate, achieving an impressive Mean Absolute Error (MAE) of 0.03%. The research findings bring out the complexity of temporal patterns of energy use, bringing out those key variables that affect electricity demand and supply.