کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
8071695 | 1521398 | 2018 | 10 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Medium-term heat load prediction for an existing residential building based on a wireless on-off control system
ترجمه فارسی عنوان
پیش بینی جریان متوسط حرارت برای یک ساختمان مسکونی موجود بر اساس یک سیستم کنترل بی سیم بی سیم
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کلمات کلیدی
سیستم گرمایش مرکزی، بار حرارتی، پیش بینی، ماشین بردار پشتیبانی، شبکه عصبی،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی انرژی
انرژی (عمومی)
چکیده انگلیسی
For district heating systems, prediction of the heat load is a very important topic for energy storage and optimized operation. For large and complex heating systems, most prediction models in previous publications only considered the influence of outdoor temperature, whereas the indoor temperature and thermal inertia of buildings were not included. For an energy-efficient residential building in Shijiazhuang (China), the heat load prediction is investigated using various prediction models, including a wavelet neural network (WNN), extreme learning machine (ELM), support vector machine (SVM) and back propagation neural network optimized by a genetic algorithm (GA-BP). In these models, the indoor temperature and historical loads are considered as influencing factors. It is found that the prediction accuracies of the ELM and GA-BP are slightly higher than that of WNN, so the ELM and GA-BP models provide feasible methods for the heat load prediction. The SVM shows smaller relative errors in the model prediction compared with three neural network algorithms.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Energy - Volume 152, 1 June 2018, Pages 709-718
Journal: Energy - Volume 152, 1 June 2018, Pages 709-718
نویسندگان
Jihao Gu, Jin Wang, Chengying Qi, Chunhua Min, Bengt Sundén,