Solar photovoltaic (PV) systems are relatively new and there is not a large amount of performance data available for them with which to compare design calculations. This comparison is also necessary to provide confidence that newer systems will perform as predicted. This chapter describes a year's monitoring of a 60 kW PV system near Strathmore, Alberta, latitude 51°, installed in November 2016. The modules were flush mounted to a roof with 8° of pitch. There was no shading and the installation was near an Alberta Department of Agriculture meteorological station which provided the weather data. The measured capacity factor was 13.8%, and there was a loss of 11%-12% of the yearly production to snow. We demonstrate that satellite-based production forecasts of the array irradiance underestimated the solar resource at this location. The predictions of actual energy production from two different modeling tools showed that the more detailed System Advisor Model software was more accurate than RETScreen.
{"title":"Performance monitoring of a 60 kW photovoltaic array in Alberta","authors":"O. Treacy, D. Wood","doi":"10.1049/PBPO155E_CH3","DOIUrl":"https://doi.org/10.1049/PBPO155E_CH3","url":null,"abstract":"Solar photovoltaic (PV) systems are relatively new and there is not a large amount of performance data available for them with which to compare design calculations. This comparison is also necessary to provide confidence that newer systems will perform as predicted. This chapter describes a year's monitoring of a 60 kW PV system near Strathmore, Alberta, latitude 51°, installed in November 2016. The modules were flush mounted to a roof with 8° of pitch. There was no shading and the installation was near an Alberta Department of Agriculture meteorological station which provided the weather data. The measured capacity factor was 13.8%, and there was a loss of 11%-12% of the yearly production to snow. We demonstrate that satellite-based production forecasts of the array irradiance underestimated the solar resource at this location. The predictions of actual energy production from two different modeling tools showed that the more detailed System Advisor Model software was more accurate than RETScreen.","PeriodicalId":443101,"journal":{"name":"Energy Generation and Efficiency Technologies for Green Residential Buildings","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131911352","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. McCoy, Dong Zhao, Yunjeong Mo, P. Agee, Frederick Paige
Residential buildings have accounted for more than 20% of total energy usage in the United States over the last decade. Reducing household energy consumption has environmental and economic impacts. Building scientists and construction engineers have attempted to obtain accurate energy use prediction; however, few have focused on the relationship between construction cost and energy use. This chapter investigates the associations among detailed construction cost takeoffs and actual energy use in multifamily green buildings. The researchers employ advanced machine-learning analytics to model the correlations between construction costs and energy use data collected from multifamily residential units. The findings identify cost divisions in the construction stage that significantly correlate with energy use in the operational stage. The model allows developers to predict energy consumption based on construction costs and enables them to adjust their investment strategies to amplify the energy efficiency of green building technologies.
{"title":"Latent relationships between construction cost and energy efficiency in multifamily green buildings","authors":"A. McCoy, Dong Zhao, Yunjeong Mo, P. Agee, Frederick Paige","doi":"10.1049/PBPO155E_CH8","DOIUrl":"https://doi.org/10.1049/PBPO155E_CH8","url":null,"abstract":"Residential buildings have accounted for more than 20% of total energy usage in the United States over the last decade. Reducing household energy consumption has environmental and economic impacts. Building scientists and construction engineers have attempted to obtain accurate energy use prediction; however, few have focused on the relationship between construction cost and energy use. This chapter investigates the associations among detailed construction cost takeoffs and actual energy use in multifamily green buildings. The researchers employ advanced machine-learning analytics to model the correlations between construction costs and energy use data collected from multifamily residential units. The findings identify cost divisions in the construction stage that significantly correlate with energy use in the operational stage. The model allows developers to predict energy consumption based on construction costs and enables them to adjust their investment strategies to amplify the energy efficiency of green building technologies.","PeriodicalId":443101,"journal":{"name":"Energy Generation and Efficiency Technologies for Green Residential Buildings","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125499615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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