“Smart cities” start with “Smart Buildings” that improve the quality of urban services while ensuring sustainability. The current scenario in India reveals that the corporate and residential building structures are incorporating various self-sustainable techniques. Out of the multiple factors governing the comfort of smart buildings, indoor room temperature is an important one, since it drives the need of cooling or heating through controlling systems. Around one-third of total energy consumption of commercial buildings in India is attributed to Heating, Ventilation and Air Conditioning (HVAC) systems. Accurate prediction of indoor room temperature helps in creating an efficient equilibrium between energy consumption and comfort level of the building, thus providing opportunities for efficient decision making for energy optimization. Considering Indian climatic and geographical conditions, this paper proposes an efficient decision making approach using Bayesian Dynamic Models (BDM) for short-term indoor room temperature forecasting of a corporate building structure. The results obtained from Bayesian Dynamic linear model, using Expectation Maximization (EM) algorithm, have been compared to standard Auto Regressive Integrated Moving Average (ARIMA) model, and have been found to be more accurate. Forecasting of indoor room temperature is a highly nonlinear phenomenon, so to further improve the accuracy of the linear models, a hybrid modeling approach has been proposed. The inclusion of state-of-the-art nonlinear models such as Artificial Neural Networks (ANNs) and Support Vector Regression (SVR) improves the forecasting accuracy of the linear models significantly. Results show that the hybrid model obtained using BDM and ANN is the best fit model.

中文翻译：

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智能环境中短期室内室温预测的有效决策方法：来自印度的证据

“智慧城市”始于“智慧建筑”，可提高城市服务质量，同时确保可持续性。印度目前的情况表明，企业和住宅建筑结构正在采用各种自我可持续的技术。在影响智能建筑舒适度的众多因素中，室内温度是一个重要因素，因为它通过控制系统驱动冷却或加热的需求。印度商业建筑总能耗中约有三分之一来自供暖、通风和空调 (HVAC) 系统。室内温度的准确预测有助于在建筑物的能耗和舒适度之间建立有效的平衡，从而为能源优化的有效决策提供机会。考虑到印度的气候和地理条件，本文提出了一种使用贝叶斯动态模型 (BDM) 对企业建筑结构进行短期室内室温预测的有效决策方法。使用期望最大化 (EM) 算法从贝叶斯动态线性模型获得的结果已与标准自回归综合移动平均 (ARIMA) 模型进行比较，发现其更准确。室内室温预测是一种高度非线性的现象，因此为了进一步提高线性模型的准确性，提出了一种混合建模方法。包含最先进的非线性模型，如人工神经网络 (ANN) 和支持向量回归 (SVR)，显着提高了线性模型的预测准确性。