کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6765903 512439 2016 11 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Improvement in artificial neural network-based estimation of grid connected photovoltaic power output
ترجمه فارسی عنوان
بهبود در برآورد مبتنی بر شبکه عصبی مصنوعی خروجی قدرت فتوولتائیک متصل به شبکه
موضوعات مرتبط
مهندسی و علوم پایه مهندسی انرژی انرژی های تجدید پذیر، توسعه پایدار و محیط زیست
چکیده انگلیسی
This paper presents a method to improve the accuracy of artificial neural network (ANN)-based estimation of photovoltaic (PV) power output by introducing two more inputs, solar zenith angle and solar azimuth angle, in addition to the most widely used environmental information, plane-of-array irradiance and module temperature. Solar zenith angle and solar azimuth angle define the solar position in the sky; hence, the loss of modeling accuracy due to impacts of solar angle-of-incidence and solar spectrum is reduced or eliminated. The observed data from two sites where local climates are significantly different is used to train and test the proposed network. The good performance of the proposed network is verified by comparing with existing ANN model, algebraic model, and polynomial regression model which use environmental information only of plane-of-array irradiance and module temperature. Our results show that the proposed ANN model greatly improves the accuracy of estimation in the long term under various weather conditions. It is also demonstrated that the improvement in estimating outdoor PV power output by adding solar zenith angle and azimuth angle as inputs is useful for other data-driven methods like support vector machine regression and Gaussian process regression.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Renewable Energy - Volume 97, November 2016, Pages 838-848
نویسندگان
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