The energy consumption in the residential sector has been increased steadily and occupied approximately 30–40% of overall energy consumption. Recent researches on energy consumption have highlighted the significance of residential building energy consumption forecast for enhanced decision‐making in terms of an energy conservation plan. Therefore, it is essential to predict the energy consumption of a residential building by developing a precise prediction model with 95% coefficient bounds. In this paper, an energy consumption data‐driven prediction model is developed using the artificial neural network (ANN) and TRNSYS software. This ANN model is trained with deep learning by using the Levenberg–Marquardt backpropagation algorithm. A 2BHK single‐story multizone residential building having six zones (two bedrooms, one living room, one kitchen, and two toilets) has been modeled in TRNSYS to estimate the energy consumption based on predicted temperature and humidity. First, the data mining technique is used to discover and summarize the historical weather data for temperature and relative humidity prediction. Secondly, the cooling and heating energy consumption has been estimated based on predicted relative humidity and temperature in TRNSYS. In contrast, the energy consumption of ventilation and lighting system is calculated mathematically based on SP 41 standard.

中文翻译：

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到2050年的基于ANN的住宅建筑能耗预测模型：朝着可持续决策的方向发展

住宅部门的能源消耗稳步增长，约占总能源消耗的30-40％。近期的能源消耗研究突出了住宅建筑能耗预测对于根据节能计划进行增强决策的重要性。因此，通过开发具有95％系数范围的精确预测模型来预测住宅建筑物的能耗至关重要。在本文中，使用人工神经网络（ANN）和TRNSYS软件开发了能耗数据驱动的预测模型。该神经网络模型通过使用Levenberg-Marquardt反向传播算法进行深度学习训练。2BHK单层多区域住宅建筑，具有六个区域（两间卧室，一间客厅，一个厨房，和两个厕所）已在TRNSYS中建模，以根据预测的温度和湿度估算能耗。首先，数据挖掘技术用于发现和总结历史天气数据，以进行温度和相对湿度的预测。其次，根据TRNSYS中预测的相对湿度和温度估算了制冷和制热能耗。相反，通风和照明系统的能耗是根据SP 41标准进行数学计算的。根据TRNSYS中预测的相对湿度和温度估算了冷却和加热能耗。相反，通风和照明系统的能耗是根据SP 41标准进行数学计算的。根据TRNSYS中预测的相对湿度和温度估算了冷却和加热能耗。相反，通风和照明系统的能耗是根据SP 41标准进行数学计算的。