کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
7935470 1513055 2018 13 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Using artificial neural networks to assess HVAC related energy saving in retrofitted office buildings
ترجمه فارسی عنوان
با استفاده از شبکه های عصبی مصنوعی برای ارزیابی هزینه های صرفه جویی در سیستم های تهویه مطبوع در ساختمان های اداری تکمیل شده
موضوعات مرتبط
مهندسی و علوم پایه مهندسی انرژی انرژی های تجدید پذیر، توسعه پایدار و محیط زیست
چکیده انگلیسی
This study aims to develop prediction models for HVAC related energy saving in office buildings. The data-driven modelling makes use of data gathered from several energy audit reports. These reports entail building and energy consumption data for 56 office buildings in Singapore. The two models are developed using Multiple Linear Regression (MLR) and Artificial Neural Network (ANN). The methodology to select the most appropriate input variables forms the essence of this study. This variable selection procedure involves 819,150 iterations, taking all possible combinations of the 14 input variables to determine the most accurate model. The dependent variable is taken as the change in energy use intensity (EUI, measured in kWh/m2.year) between pre- and post-retrofit conditions. The results show that the ANN model is more accurate with a mean absolute percentage error (MAPE) of 14.8%. The best combination of variables to achieve this comprises of gross floor area (GFA), air-conditioning energy consumption, operational hours and chiller plant efficiency. The information on these four variables, along with the prediction model can be used to predict HVAC related energy savings in office buildings to be retrofitted.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Solar Energy - Volume 163, 15 March 2018, Pages 32-44
نویسندگان
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