Consumer Load Behavior Modeling Using AI for Demand-Side Energy Management

Abstract

The accelerated increase in electricity demand levels and consequently the sophistication of contemporary power systems has placed much pressure on the effective management of energy needs on demand side. The proper knowledge and forecasting of the consumer load behaviour has become critical to enhancing the use of energy efficiency, peak reduction, and stability of the smart grid infrastructures. Machine learning methods and artificial intelligence (AI) can provide a potent means of analysing big amounts of data on energy consumption and being able to distinguish between complicated usage patterns that simple forecasting techniques are often unable to reveal. This paper develops an AI-based consumer load behaviour model that should improve demand-side energy management in smart grids. The framework combines machine learning models such as Long Short-Term Memory (LSTM), Random Forest (RF) and Artificial Neural Networks (ANN) to process historical electricity consumption data and come up with correct load forecasts. The results of the proposed models are measured by the commonly used metrics of prediction accuracy which include: Mean Absolute Error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE) and Coefficient of Determination (R 2 ). The results of the experiments prove that machine learning-based models have a strong effect on predicting loads in comparison to the traditional methods. The results point to the promise of AI-based consumer load modelling in assisting the pursuit of smart demand-side management strategies to enable better energy use and help in the creation of more efficient and sustainable smart grid solutions.