Abstract: Energy consumption forecasting is necessary for the planning process and predicting electric consumption. It plays a key role in building an efficient energy system for power generation, distribution, and sustainable consumption. An accurate forecast of electric load is essential for a power system to be planned properly, generation to be scheduled, and electricity to be delivered economically. The complexity and non-stationary of the electricity load caused by industrial growth, urbanization, and changes in lifestyle in households make applying the classical statistical and rule-based forecasting methods difficult. Customary methods are not able to capture the nonlinear fluctuation in the load, long-range dependencies in time and the specific patterns in the various sectors, leading to wastage and uncertainty in operations. To this end, the present work proposes a time- series based energy consumption forecasting system that utilizes historical load data to forecast the future electricity demand for industry and household sectors. The system follows time- series modeling techniques to analyze historical consumption data and seasonal trends of load variations. The model can also be used to forecast consumption for each sector because the consumption of industrial and residential sectors varies both in demand and in terms of frequency. Long Short-Term Memory (LSTM), a deep learning model, can be implemented to capture the time series dependencies. From the experimental results, the proposed model has been proven to perform customary statistical forecasting approaches in terms of prediction accuracy and stability. The proposed model is able to recognize peak demand periods and provide reliable demand forecasts for load management on time. The optimal prediction of demand plays an essential role in effective power generation and consumption management for power utilities and policymakers to ensure sustainable operation.

Index Terms: Energy Forecasting, Time-Series Analysis, ARIMA, LSTM, Load Prediction, Power Systems.

Downloads: | DOI: 10.17148/IARJSET.2026.13414