در استفاده از شبکه های عصبی مصنوعی برای مدل سازی مصرف انرژی خانوار
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|89784||2017||8 صفحه PDF||سفارش دهید|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Energy Procedia, Volume 126, September 2017, Pages 250-257
Modern houses are more and more frequently characterized by the presence of âsmartâ metering devices, capable of measuring air temperature, relative humidity, air quality, and in the more sophisticated cases, even electric equipment consumptions. In addition, other relevant parameters such as illuminance may often be determined and they can be used as proxy variables to account for other important aspects (such as solar irradiance) influencing the energy balance of a building. Such information, in combination with weather data which can be retrieved by other sources (or by additional sensors), may conveniently contribute to the creation of a âblack boxâ model in which, given a few input variables it is possible to output a variable which would result from otherwise complex calculations (e.g. an energy balance) requiring many data. The availability of such a âblack boxâ could be helpful under many points of view, such as benchmarking energy consumptions and stimulating virtuous behavior from the occupants. To test whether such approach can be feasible, an EnergyPlus model of a real house was made, trying to accurately reproduce building features, systems set-points, and occupant behaviors. The overall simulated energy consumptions were compared with the real ones resulting from energy bills, thus ensuring a good agreement with reality. The dataset resulting from EnergyPlus was then used to train an artificial neural network (ANN) capable of yielding hourly energy consumptions based on limited input data. Finally, the relative importance of the different input variables was analyzed to understand which might influence prediction accuracy most.