Advanced building controls and energy optimization for new constructions and retrofits rely on accurate weather data.Traditionally,most studies utilize airport weather information as the decision inputs.However,most b...Advanced building controls and energy optimization for new constructions and retrofits rely on accurate weather data.Traditionally,most studies utilize airport weather information as the decision inputs.However,most buildings are in environments that are quite different than those at the airport miles away.Tree cover,adjacent buildings,and micro-climate effects caused by the larger surrounding area can all yield deviations in air temperature,humidity,solar irradiance,and wind that are large enough to influence design and operation decisions.In order to overcome this challenge,there are many prior studies on developing weather forecasting algorithms from micro-to meso-scales.This paper reviews and complies knowledge on common weather data resources,data processing methodologies and forecasting techniques of weather information.Commonly used statistical,machine learning and physical-based models are discussed and presented as two major categories:deterministic forecasting and probabilistic forecasting.Finally,evaluation metrics for forecasting errors are listed and discussed.展开更多
Weather forecasting has been a critical component to predict and control building energy consumption for better building energy management.Without accessibility to other data sources,the onsite observed temperatures o...Weather forecasting has been a critical component to predict and control building energy consumption for better building energy management.Without accessibility to other data sources,the onsite observed temperatures or the airport temperatures are used in forecast models.In 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 framework.The final forecast is based on the ensemble of local forecasts for the target location using neighboring PWSs.Our approach is distinguished from existing literature in various aspects.First,we leverage the crowdsourcing weather data from PWS in addition to public data sources.In 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 performance.We 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,USA.In 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 forecasting.In a real-time setting,the proposed model framework can provide more accurate temperature forecasting results compared with using airport temperature forecast for most forecast horizon.Moreover,we analyze the sensitivity of model parameters to gain insights on how crowdsourcing data from the neighboring personal weather stations impacts forecasting performance.Finally,we implement our model in other cities such as Syracuse and Chicago to test the model’s performance in different landforms and climate types.展开更多
基金This work was 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 under Contract No.DE AC02-06CH11357The 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.
文摘Advanced building controls and energy optimization for new constructions and retrofits rely on accurate weather data.Traditionally,most studies utilize airport weather information as the decision inputs.However,most buildings are in environments that are quite different than those at the airport miles away.Tree cover,adjacent buildings,and micro-climate effects caused by the larger surrounding area can all yield deviations in air temperature,humidity,solar irradiance,and wind that are large enough to influence design and operation decisions.In order to overcome this challenge,there are many prior studies on developing weather forecasting algorithms from micro-to meso-scales.This paper reviews and complies knowledge on common weather data resources,data processing methodologies and forecasting techniques of weather information.Commonly used statistical,machine learning and physical-based models are discussed and presented as two major categories:deterministic forecasting and probabilistic forecasting.Finally,evaluation metrics for forecasting errors are listed and discussed.
文摘Weather forecasting has been a critical component to predict and control building energy consumption for better building energy management.Without accessibility to other data sources,the onsite observed temperatures or the airport temperatures are used in forecast models.In 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 framework.The final forecast is based on the ensemble of local forecasts for the target location using neighboring PWSs.Our approach is distinguished from existing literature in various aspects.First,we leverage the crowdsourcing weather data from PWS in addition to public data sources.In 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 performance.We 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,USA.In 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 forecasting.In a real-time setting,the proposed model framework can provide more accurate temperature forecasting results compared with using airport temperature forecast for most forecast horizon.Moreover,we analyze the sensitivity of model parameters to gain insights on how crowdsourcing data from the neighboring personal weather stations impacts forecasting performance.Finally,we implement our model in other cities such as Syracuse and Chicago to test the model’s performance in different landforms and climate types.
