The Experts below are selected from a list of 12372 Experts worldwide ranked by ideXlab platform
Clayton Miller - One of the best experts on this subject based on the ideXlab platform.
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mining Electrical Meter data to predict principal building use performance class and operations strategy for hundreds of non residential buildings
Energy and Buildings, 2017Co-Authors: Clayton Miller, Forrest MeggersAbstract:Abstract This study focuses on the inference of characteristic data from a data set of 507 non-residential buildings. A two-step framework is presented that extracts statistical, model-based, and pattern-based behavior. The goal of the framework is to reduce the expert intervention needed to utilize measured raw data in order to infer information such as building use type, performance class, and operational behavior. The first step is temporal feature extraction, which utilizes a library of data mining techniques to filter various phenomenon from the raw data. This step transforms quantitative raw data into qualitative categories that are presented in heat map visualizations for interpretation. In the second step, a random forest classification model is tested for accuracy in predicting primary space use, magnitude of energy consumption, and type of operational strategy using the generated features. The results show that predictions with these methods are 45.6% more accurate for primary building use type, 24.3% more accurate for performance class, and 63.6% more accurate for building operations type as compared to baselines.
Forrest Meggers - One of the best experts on this subject based on the ideXlab platform.
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mining Electrical Meter data to predict principal building use performance class and operations strategy for hundreds of non residential buildings
Energy and Buildings, 2017Co-Authors: Clayton Miller, Forrest MeggersAbstract:Abstract This study focuses on the inference of characteristic data from a data set of 507 non-residential buildings. A two-step framework is presented that extracts statistical, model-based, and pattern-based behavior. The goal of the framework is to reduce the expert intervention needed to utilize measured raw data in order to infer information such as building use type, performance class, and operational behavior. The first step is temporal feature extraction, which utilizes a library of data mining techniques to filter various phenomenon from the raw data. This step transforms quantitative raw data into qualitative categories that are presented in heat map visualizations for interpretation. In the second step, a random forest classification model is tested for accuracy in predicting primary space use, magnitude of energy consumption, and type of operational strategy using the generated features. The results show that predictions with these methods are 45.6% more accurate for primary building use type, 24.3% more accurate for performance class, and 63.6% more accurate for building operations type as compared to baselines.
Steven B Leeb - One of the best experts on this subject based on the ideXlab platform.
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Nonintrusive Load Monitoring of Variable Speed Drive Cooling Systems
IEEE Access, 2020Co-Authors: Peter A. Lindahl, Muhammad Tauha Ali, Peter Armstrong, Andre Aboulian, John Donnal, Les Norford, Steven B LeebAbstract:To improve the energy efficiencies of building cooling systems, manufacturers are increasingly utilizing variable speed drive (VSD) motors in system components, e.g. compressors and condensers. While these technologies can provide significant energy savings, these benefits are only realized if these components operate as intended and under proper control. Undetected faults can foil efficiency gains. As such, it's imperative to monitor cooling system performance to both identify faulty conditions and to properly inform building or multi-building models used for predictive control and energy management. This paper presents nonintrusive load monitoring (NILM) based “mapping” techniques for tracking the performance of a building's central air conditioning from smart Electrical Meter or energy monitor data. Using a multivariate linear model, a first mapping disaggregates the air conditioner's power draw from that of the total building by exploiting the correlations between the building's line-current harmonics and the power consumption of the air conditioner's VSD motors. A second mapping then estimates the air conditioner's heat rejection performance using as inputs the estimated power draw of the first mapping, the building's zonal temperature, and the outside environmental temperature. The usefulness of these mapping techniques are demonstrated using data collected from a research facility building on the Masdar City Campus of Khalifa University. The mapping techniques combine to provide accurate estimates of the building's air conditioning performance when operating under normal conditions. These estimates could thus be used as feedback in building energy management controllers and can provide a performance baseline for detection of air conditioner underperformance.
Peter A. Lindahl - One of the best experts on this subject based on the ideXlab platform.
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Nonintrusive Load Monitoring of Variable Speed Drive Cooling Systems
IEEE Access, 2020Co-Authors: Peter A. Lindahl, Muhammad Tauha Ali, Peter Armstrong, Andre Aboulian, John Donnal, Les Norford, Steven B LeebAbstract:To improve the energy efficiencies of building cooling systems, manufacturers are increasingly utilizing variable speed drive (VSD) motors in system components, e.g. compressors and condensers. While these technologies can provide significant energy savings, these benefits are only realized if these components operate as intended and under proper control. Undetected faults can foil efficiency gains. As such, it's imperative to monitor cooling system performance to both identify faulty conditions and to properly inform building or multi-building models used for predictive control and energy management. This paper presents nonintrusive load monitoring (NILM) based “mapping” techniques for tracking the performance of a building's central air conditioning from smart Electrical Meter or energy monitor data. Using a multivariate linear model, a first mapping disaggregates the air conditioner's power draw from that of the total building by exploiting the correlations between the building's line-current harmonics and the power consumption of the air conditioner's VSD motors. A second mapping then estimates the air conditioner's heat rejection performance using as inputs the estimated power draw of the first mapping, the building's zonal temperature, and the outside environmental temperature. The usefulness of these mapping techniques are demonstrated using data collected from a research facility building on the Masdar City Campus of Khalifa University. The mapping techniques combine to provide accurate estimates of the building's air conditioning performance when operating under normal conditions. These estimates could thus be used as feedback in building energy management controllers and can provide a performance baseline for detection of air conditioner underperformance.
Patrick Ienny - One of the best experts on this subject based on the ideXlab platform.
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Development of a recycling solution for waste thermoset material: waste source study, comminution scheme and filler characterization
Journal of Material Cycles and Waste Management, 2018Co-Authors: Fabien Bernardeau, Didier Perrin, Jean-charles Bénézet, Anne-sophie Caro-bretelle, Patrick IennyAbstract:End of life Electrical equipment is a continuously increasing source of waste in our modern society, and constitute an environmental problem. Understanding this type of waste flow is important to devise proper dismantlement and sorting strategies, and to maximize the material recovery rate and valorization. In this work, a waste pool constituted of Electrical Meter was studied. The specificities of this equipment in term of design were enlightened, and the overall material composition was determined. An emphasis was put on the characterization of the plastic fraction, both in term of plastic type and presence of regulated substances. It revealed that this fraction is mostly composed of phenolic molding compound (PMC), a thermoset material, which is troublesome in term of recycling. A material valorization solution through mechanical recycling is proposed, consisting in using PMC as functional filler in a thermoplastic matrix. A comminution scheme to obtain such filler is presented in this work, and the comminuted products are characterized. Through 2 or 3 steps of comminution, particle size below 50 µm can be obtained, which is expected to be a sufficient size for incorporation in a thermoplastic matrix.