In this paper, the design and implementation process of an artificial neural network based predictor to forecast a day ahead of the power consumption of a building HVAC system is presented. The featured HVAC system is situated at MagicBox, a real self-sufficient solar house with a monitoring system. Day ahead prediction of HVAC power consumption will remarkably enhance the Demand Side Management techniques based on appliance scheduling to reach defined goals. Several multi step prediction models, based on LSTM neural networks, are proposed. In addition, suitable data preprocessing and arrangement techniques are set to adapt the raw dataset. Considering the targeted prediction horizon, the models provide outstanding results in terms of test errors (NRMSE of 0.13) and correlation, between the temporal behavior of the predictions and test time series to be forecasted, of 0.797. Moreover, these results are compared to the simplified one hour ahead prediction that reaches nearly optimal test NRMSE of 0.052 and Pearson correlation coefficient of 0.972. These results provide an encouraging perspective for real-time energy consumption prediction in buildings.

本文提出了一种基于人工神经网络的预测器的设计和实现过程，该预测器可以预测建筑物HVAC系统的功耗提前一天。特色的HVAC系统位于MagicBox，这是一个真正的自给自足的带有监控系统的太阳能房屋。HVAC功耗的前一天预测将显着增强基于设备调度的需求方管理技术，以实现既定目标。提出了几种基于LSTM神经网络的多步预测模型。另外，设置了合适的数据预处理和排列技术以适应原始数据集。考虑到目标预测范围，这些模型在测试误差（NRMSE为0.13）和相关性方面提供了出色的结果，预测的时间行为与要预测的测试时间序列之间的关系为0.797。此外，将这些结果与简化的提前一小时预测相比较，该预测几乎达到了最佳测试NRMSE为0.052和Pearson相关系数为0.972。这些结果为建筑物中的实时能耗预测提供了令人鼓舞的前景。