Globally, the construction industry is experiencing an increase in energy demand, which has significant environmental and economic repercussions. To address these issues, it is now possible for buildings, vehicles, and renewable energy sources to collaborate and function as an advanced, integrated, and environmentally favorable system that meets the high energy demands of contemporary buildings. To attain maximum efficiency, however, it is necessary to create reliable energy demand forecasting models. In this research, by introducing the energy model of a neighbourhood with buildings with solar panels and electric vehicles, the final balance of energy production and consumption for each building and the whole neighbourhood as a micro grid is predicted. DesignBuilder is used to model neighbourhood buildings, and K-Nearest neighbor (KNN), Regression Support Vector (SVR), Adaptive Boosting (AdaBoost), and Deep neural networks (DNN) algorithms in machine learning are used to predict the final energy balance. a comparative analysis of the performance of the KNN, SVR, AdaBoost, and DNN algorithms was conducted to determine which algorithm is the most effective in predicting energy balance. Finally, the Root Mean Square Error (RMSE) has been used to validate the prediction models. The results show that the KNN, SVR, AdaBoost, and DNN algorithms had RMSE values of 0.56, 0.92, 0.95, and 0.53, respectively. Among these algorithms, the DNN and KNN algorithms had more accurate results than the other used algorithms and as a result of this research, An accurate forecast of neighbourhood energy balance was made. This study takes a novel approach by developing a model that takes into account an integrated system of houses, solar cells, and electric consumption for each building in a neighborhood, which can help to optimize energy consumption and reduce environmental impact.

中文翻译：

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混合建筑-可再生能源系统中能量平衡预测的机器学习和深度学习方法的比较研究

在全球范围内，建筑行业的能源需求正在增加，这对环境和经济产生了重大影响。为了解决这些问题，建筑物、车辆和可再生能源现在可以协作并作为先进、集成且环保的系统发挥作用，满足当代建筑的高能源需求。然而，为了实现最大效率，有必要创建可靠的能源需求预测模型。在这项研究中，通过引入具有太阳能电池板和电动汽车的建筑物的社区的能源模型，预测每栋建筑物和整个社区作为微电网的能源生产和消耗的最终平衡。DesignBuilder用于对邻里建筑物进行建模，以及K最近邻（KNN），机器学习中的回归支持向量（SVR）、自适应提升（AdaBoost）和深度神经网络（DNN）算法用于预测最终的能量平衡。对 KNN、SVR、AdaBoost 和 DNN 算法的性能进行了比较分析，以确定哪种算法在预测能量平衡方面最有效。最后，均方根误差（RMSE）被用来验证预测模型。结果表明，KNN、SVR、AdaBoost 和 DNN 算法的 RMSE 值分别为 0.56、0.92、0.95 和 0.53。在这些算法中，DNN 和 KNN 算法比其他使用的算法具有更准确的结果，并且作为本研究的结果，对邻域能量平衡进行了准确的预测。