کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
263054 504063 2014 13 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Pseudo dynamic transitional modeling of building heating energy demand using artificial neural network
ترجمه فارسی عنوان
مدل سازی انتقالی پویا برای تقاضای انرژی گرمایشی با استفاده از شبکه عصبی مصنوعی
کلمات کلیدی
پیش بینی انرژی ساختمان، پیش بینی انرژی کوتاه مدت ساختمان، ویژگی های حرارت عملیاتی، مشخصات مسکن شبکه های عصبی مصنوعی، آرایه های متعامد
موضوعات مرتبط
مهندسی و علوم پایه مهندسی انرژی انرژی های تجدید پذیر، توسعه پایدار و محیط زیست
چکیده انگلیسی


• Novel pseudo dynamic transitional model is introduced.
• Orthogonal array design is applied to the pseudo dynamic model.
• A large building is considered for case study.
• Minimum energy consumption error is achieved and is 0.02% for learning phase and 2.39% for validation phase.
• Application for energy operator to manage the heating load for dynamic control of heat production system.

This paper presents the building heating demand prediction model with occupancy profile and operational heating power level characteristics in short time horizon (a couple of days) using artificial neural network. In addition, novel pseudo dynamic transitional model is introduced, which consider time dependent attributes of operational power level characteristics and its effect in the overall model performance is outlined. Pseudo dynamic model is applied to a case study of French Institution building and compared its results with static and other pseudo dynamic neural network models. The results show the coefficients of correlation in static and pseudo dynamic neural network model of 0.82 and 0.89 (with energy consumption error of 0.02%) during the learning phase, and 0.61 and 0.85 during the prediction phase, respectively. Further, orthogonal array design is applied to the pseudo dynamic model to check the schedule of occupancy profile and operational heating power level characteristics. The results show the new schedule and provide the robust design for pseudo dynamic model. Due to prediction in short time horizon, it finds application for Energy Services Company (ESCOs) to manage the heating load for dynamic control of heat production system.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Energy and Buildings - Volume 70, February 2014, Pages 81–93
نویسندگان
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