DECODE: Data-driven energy consumption prediction leveraging historical data and environmental factors in buildings.

Energy prediction in buildings plays a crucial role in effective energy management. Precise predictions are essential for achieving optimal energy consumption and distribution within the grid. This paper introduces a Long Short-Term Memory (LSTM) model designed to forecast building energy consumption using historical energy data, occupancy patterns, and weather conditions. The LSTM model provides accurate short, medium, and long-term energy predictions for residential and commercial buildings compared to existing prediction models. We compare our LSTM model with established prediction methods, including linear regression, decision trees, and random forest. Encouragingly, the proposed LSTM model emerges as the superior performer across all metrics. It demonstrates exceptional prediction accuracy, boasting the highest R 2 score of 0.97 and the most favorable mean absolute error (MAE) of 0.007. An additional advantage of our developed model is its capacity to achieve efficient energy consumption forecasts even when trained on a limited dataset. We address concerns about overfitting (variance) and underfitting (bias) through rigorous training and evaluation on real-world data. In summary, our research contributes to energy prediction by offering a robust LSTM model that outperforms alternative methods and operates with remarkable efficiency, generalizability, and reliability. • The proposed LSTM model forecasts accurate and reliable short, medium, and long-term energy consumption predictions. • The proposed LSTM model is capable of energy consumption predictions in both residential and commercial buildings. • The proposed LSTM requires a minimal amount of training data to forecast accurately and uses a variety of features including weather and occupancy data for precise energy consumption prediction. • The model achieves an average R2-score of 0.91 and average MAE of 0.013. [ABSTRACT FROM AUTHOR]