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 R2 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.

本研究通过使用历史能源数据、占用模式和天气条件，引入了一种基于长短期记忆（LSTM）模型的建筑能源消耗预测方法。该 LSTM 模型对于住宅和商业建筑的短期、中期和长期能源预测相比现有的预测模型具有更高的准确性。通过与线性回归、决策树和随机森林等已有预测方法的比较，我们的 LSTM 模型在所有指标上表现出色，具有 0.97 的最高 R2 得分和 0.007 的最佳平均绝对误差（MAE）。我们的模型还具有在受限数据集上实现高效能源消耗预测的能力。通过在实际数据上的严格训练和评估，我们解决了过拟合（方差）和欠拟合（偏差）的问题。总结而言，我们的研究通过提供一种优于其他方法的、具有出色效率、普适性和可靠性的强大 LSTM 模型，为能源预测做出了贡献。