Forecasting underheating in dwellings to detect excess winter mortality risks using time series models
Forecasting underheating in dwellings to detect excess winter mortality risks using time series models
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DOI：
10.1016/j.apenergy.2021.116517
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发表时间：
2021-03
期刊：
影响因子：
11.2
通讯作者：
A. Ahmed;R. McLeod;Matej Gustin
中科院分区：
文献类型：
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作者：
A. Ahmed;R. McLeod;Matej Gustin
对住宅即将到来的低温进行预先预报，可以在实时预测能源贫困方面发挥变革性作用，从而有助于防止冬季发病率和死亡率过高。提出了一种新的递归时间序列模型，结合自回归和外源输入，提供了多步预测的冬季室内温度的家庭。采用逐步回归方法，根据赤池信息准则的最小化自动进行最佳模型选择过程。该模型进行了验证，使用三个案例研究位于英国拉夫堡的家园。预测区间，在95%的概率水平，被用来定义一个可信区间的预测温度在不同的时间范围内，在寒冷的天气。具有外源输入的自回归模型被证明能够提前1、3和6小时产生可靠的预报，这些视野的平均绝对误差低于1.38 °C。结果表明，该模型始终优于更复杂的自回归移动平均与外源输入模型。该研究提供了第一个证据，证明了使用时间序列预测作为高分辨率室内冬季预警响应系统的一部分的潜力，该系统可用于识别面临与寒冷相关的健康影响的房屋。
Advanced forecasting of impending low temperatures in dwellings could play a transformative role in predicting energy poverty in real-time, thereby helping to prevent excess winter morbidity and mortality. A novel recursive time series model combining AutoRegressive with eXogenous inputs was developed to provide multi-step ahead predictions of the wintertime internal temperatures of homes. A stepwise regression approach was adopted to automate the optimal model selection process based on the minimisation of the Akaike Information Criterion. The model was validated using three case study homes located in Loughborough, UK. Prediction intervals, at the 95% probability level, were used to define a credible interval for the forecasted temperatures at different time horizons during periods of cold weather. The AutoRegressive with eXogenous inputs model proved capable of producing reliable forecasts for 1, 3 and 6 h ahead, achieving Mean Absolute Errors below 1.38 °C for these horizons. The results showed that this model consistently outperformed the more complex AutoRegressive Moving Average with eXogenous inputs model. The study provides the first evidence of the potential for using time series forecasting as part of a high-resolution indoor Winter Early Warning Response System which could be used to identify homes at imminent risk of cold-related health impacts.