Energy Demand Forecasting Model Using Artificial Neural Networks: A Case Study of a Faculty Building in Tehran
محل انتشار: کنگره بین المللی علوم و مهندسی
سال انتشار: 1396
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 381
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شناسه ملی سند علمی:
GERMANCONF01_219
تاریخ نمایه سازی: 26 مرداد 1397
چکیده مقاله:
In this paper, the natural gas demand for an educational building in Tehran (Sharif University of Technology) is modeled using the artificial neural networks. An artificial neural network is a very powerful and intelligent tool that can be used to model and identify nonlinear and complex systems. According to the results obtained from this modeling, the accuracy of the natural gas demand forecasting for a typical day is more than 85 percent. The advantages of the model used in this paper are to consider the influence of climate parameters (ambient temperature, relative humidity, radiation intensity and wind speed) and the number of inhabitants.
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نویسندگان
Mohammadbagher Hamidi
Energy Systems Engineering, Sharif University of Technology
Rahim Moltames
Energy Systems Engineering, Sharif University of Technology
Aref Ghabai
Energy Systems Engineering, Shahid Beheshti University