A general spatial-temporal framework for short-term building temperature forecasting at arbitrary locations with crowdsourcing weather data

作     者：Reisa F.Widjaja Wenbo Wu Zhi Zhou Renhao Sun Hannah C.Fontenot Bing Dong Reisa F.Widjaja;Wenbo Wu;Zhi Zhou;Renhao Sun;Hannah C.Fontenot;Bing Dong

作者机构：Department of Management Science&StatisticsThe University of Texas at San AntonioSan AntonioTX78249USA Energy Systems DivisionArgonne National LaboratoryLemontIL60439USA Department of Mechanical&Aerospace EngineeringSyracuse UniversitySyracuseNY13244USA 

出 版 物：《Building Simulation》 (建筑模拟（英文）)

年 卷 期：2023年第16卷第6期

页      面：963-982页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0706[理学-大气科学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy through its Building Technologies Office The submitted manuscript has been created by UChicago Argonne, LLC, Operator of Argonne National Laboratory ("Argonne") Argonne, a U.S. Department of Energy Office of Science laboratory, is operated The views expressed in this article are the authors' own and do not necessarily represent the views of the U.S. Department of Energy or the United States Government 

主　　题：crowdsourcing data machine learning spatial temporal model weather forecasting 

摘      要：Weather forecasting has been a critical component to predict and control building energy consumption for better building energy *** accessibility to other data sources,the onsite observed temperatures or the airport temperatures are used in forecast *** this paper,we present a novel approach by utilizing the crowdsourcing weather data from neighboring personal weather stations(PWS)to improve the weather forecast accuracy around buildings using a general spatial-temporal modeling *** final forecast is based on the ensemble of local forecasts for the target location using neighboring *** approach is distinguished from existing literature in various ***,we leverage the crowdsourcing weather data from PWS in addition to public data *** this way,the data is at much finer time resolution(e.g.,at 5-minute frequency)and spatial resolution(e.g.,arbitrary location vs grid).Second,our proposed model incorporates spatial-temporal correlation information of weather variables between the target building and a set of neighboring PWSs so that underlying correlations can be effectively captured to improve forecasting *** demonstrate the performance of the proposed framework by comparing to the benchmark models on temperature forecasting for a building located at an arbitrary location at San Antonio,Texas,*** general,the proposed model framework equipped with machine learning technique such as Random Forest can improve forecasting by 50%compares with persistent model and has 90%chance to outperform airport forecast in short-term *** a real-time setting,the proposed model framework can provide more accurate temperature forecasting results compared with using airport temperature forecast for most forecast ***,we analyze the sensitivity of model parameters to gain insights on how crowdsourcing data from the neighboring personal weather stations impacts forecasting ***,we implement