Predict Household or Grid Electricity Consumption Using Time-Series

ABSTRACT

Energy consumption forecasting plays a critical role in optimizing fuel supply planning, resource management, and sustainable energy operations within large institutions such as household. So we focus on medium-term load forecasting for a household, aiming to predict its future energy requirements accurately and efficiently. The primary objective is to support effective consumer electricity planning and energy management strategies. To achieve this, we employ a Temporal Convolutional Network (TCN) model—an advanced deep learning architecture that utilizes one-dimensional convolutional kernels arranged in multiple layers, followed by pooling and fully connected neural network blocks. By leveraging convolutional operations instead of recurrent mechanisms, TCN effectively handles long-range dependencies while maintaining stable gradients, resulting in faster training and better scalability compared to traditional models. For performance evaluation, the TCN model is compared against two widely used time-series forecasting approaches: Long Short-Term Memory (LSTM) networks and the Autoregressive Integrated Moving Average (ARIMA) model. All three models were trained and tested on the same dataset, and their predictive accuracies were assessed using standard evaluation metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R² score. Experimental results demonstrate that the TCN model significantly outperforms both LSTM and ARIMA in terms of prediction accuracy and generalization on the unseen test data. The findings highlight the potential of Temporal Convolutional Networks as a reliable and efficient alternative for medium-term energy load forecasting in grid systems. This work contributes to the advancement of intelligent energy management systems and paves the way for more sustainable and data-driven decision-making processes in household and similar infrastructures.

Keywords: Household Electricity Consumption, Medium-Term Load Forecasting, Time-Series Prediction, TCN, LSTM, ARIMA.