Electricity energy prediction in buildings using AI

Abstract

Accurate prediction of electricity energy consumption in buildings is essential for optimizing energy usage and improving efficiency. This paper proposes a novel approach for electricity energy prediction in buildings using deep learning techniques. The model utilizes historical energy consumption data, weather conditions, and building characteristics as input features to train a deep learning algorithm. Specifically, a Long Short-Term Memory (LSTM) network is employed to capture temporal dependencies and patterns in the data. The model is trained and evaluated using a comprehensive dataset collected from a real-world building. Experimental results demonstrate the effectiveness and accuracy of the proposed approach in predicting electricity energy consumption with high precision. The developed deep learning-based prediction model holds promise in assisting building managers and energy providers in making informed decisions regarding energy usage, demand forecasting, and load management, resulting in significant energy savings and environmental benefits.