The Experts below are selected from a list of 87 Experts worldwide ranked by ideXlab platform
Jean Viaene - One of the best experts on this subject based on the ideXlab platform.
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Bayesian verification of an Energy Conservation Measure
Energy and Buildings, 2018Co-Authors: D. Lindelöf, Mohammad Alisafaee, Pierluca Borsò, Christian Grigis, Jean ViaeneAbstract:Abstract Most building owners eventually invest in Energy Conservation Measures for their buildings. Faced with a variety of options, ranging from trivial (replacing light bulbs) to major (better facade insulation), they want to know which Measure will yield the highest return on investment—and with what confidence. Moreover, once the Measure has been applied, they will want to know how well it performs and whether their money was well spent—also known as Measurement & Verification (M&V). But conventional M&V mandates the establishment of a baseline. The building should be instrumented during a year or more before applying the Measure, driving the costs of M&V to sometimes unacceptable levels. Furthermore, a typical M&V study will report a single number for the Measure’s efficiency, ignoring any uncertainty surrounding that estimate. To solve these two problems (expensive baseline and absence of uncertainty), we have developed a method, based on Bayesian statistics, that will 1) rely on historic utility bills and climate data to establish the baseline, and 2) estimate, with confidence intervals, how effective the Energy Conservation Measure was. We have tested this method after installing, in March 2016, a model-predictive controller for space heating for a medium-sized (about 2700 m2) office building in Switzerland. The baseline was established from historic oil tank refill records and climate data from a nearby weather station. The total heat loss coefficient of the building was assessed 46 days after the installation and found to have decreased by 31.9%, with 17.8 percentage points standard error.
Steven A. Gabriel - One of the best experts on this subject based on the ideXlab platform.
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An improved strategic decision-making model for Energy Conservation Measures
Energy Strategy Reviews, 2015Co-Authors: Billy R. Champion, Steven A. GabrielAbstract:Abstract Energy Conservation Measure selection is made difficult given real-world constraints, limited resources to implement savings retrofits, various suppliers in the market and project financing alternatives. The most common method of implementation planning is suboptimal. This paper presents a model that decision-makers can use to optimize the selection of Energy Conservation Measures. The practical application should supplement current best practices for agencies concerned with making the most cost-effective selection.
D. Lindelöf - One of the best experts on this subject based on the ideXlab platform.
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Bayesian verification of an Energy Conservation Measure
Energy and Buildings, 2018Co-Authors: D. Lindelöf, Mohammad Alisafaee, Pierluca Borsò, Christian Grigis, Jean ViaeneAbstract:Abstract Most building owners eventually invest in Energy Conservation Measures for their buildings. Faced with a variety of options, ranging from trivial (replacing light bulbs) to major (better facade insulation), they want to know which Measure will yield the highest return on investment—and with what confidence. Moreover, once the Measure has been applied, they will want to know how well it performs and whether their money was well spent—also known as Measurement & Verification (M&V). But conventional M&V mandates the establishment of a baseline. The building should be instrumented during a year or more before applying the Measure, driving the costs of M&V to sometimes unacceptable levels. Furthermore, a typical M&V study will report a single number for the Measure’s efficiency, ignoring any uncertainty surrounding that estimate. To solve these two problems (expensive baseline and absence of uncertainty), we have developed a method, based on Bayesian statistics, that will 1) rely on historic utility bills and climate data to establish the baseline, and 2) estimate, with confidence intervals, how effective the Energy Conservation Measure was. We have tested this method after installing, in March 2016, a model-predictive controller for space heating for a medium-sized (about 2700 m2) office building in Switzerland. The baseline was established from historic oil tank refill records and climate data from a nearby weather station. The total heat loss coefficient of the building was assessed 46 days after the installation and found to have decreased by 31.9%, with 17.8 percentage points standard error.
Billy R. Champion - One of the best experts on this subject based on the ideXlab platform.
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An improved strategic decision-making model for Energy Conservation Measures
Energy Strategy Reviews, 2015Co-Authors: Billy R. Champion, Steven A. GabrielAbstract:Abstract Energy Conservation Measure selection is made difficult given real-world constraints, limited resources to implement savings retrofits, various suppliers in the market and project financing alternatives. The most common method of implementation planning is suboptimal. This paper presents a model that decision-makers can use to optimize the selection of Energy Conservation Measures. The practical application should supplement current best practices for agencies concerned with making the most cost-effective selection.
Christian Grigis - One of the best experts on this subject based on the ideXlab platform.
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Bayesian verification of an Energy Conservation Measure
Energy and Buildings, 2018Co-Authors: D. Lindelöf, Mohammad Alisafaee, Pierluca Borsò, Christian Grigis, Jean ViaeneAbstract:Abstract Most building owners eventually invest in Energy Conservation Measures for their buildings. Faced with a variety of options, ranging from trivial (replacing light bulbs) to major (better facade insulation), they want to know which Measure will yield the highest return on investment—and with what confidence. Moreover, once the Measure has been applied, they will want to know how well it performs and whether their money was well spent—also known as Measurement & Verification (M&V). But conventional M&V mandates the establishment of a baseline. The building should be instrumented during a year or more before applying the Measure, driving the costs of M&V to sometimes unacceptable levels. Furthermore, a typical M&V study will report a single number for the Measure’s efficiency, ignoring any uncertainty surrounding that estimate. To solve these two problems (expensive baseline and absence of uncertainty), we have developed a method, based on Bayesian statistics, that will 1) rely on historic utility bills and climate data to establish the baseline, and 2) estimate, with confidence intervals, how effective the Energy Conservation Measure was. We have tested this method after installing, in March 2016, a model-predictive controller for space heating for a medium-sized (about 2700 m2) office building in Switzerland. The baseline was established from historic oil tank refill records and climate data from a nearby weather station. The total heat loss coefficient of the building was assessed 46 days after the installation and found to have decreased by 31.9%, with 17.8 percentage points standard error.