پیش بینی تولید انرژی خورشیدی
ترجمه نشده

پیش بینی تولید انرژی خورشیدی

عنوان فارسی مقاله: یک سیستم هوشمند ترکیبی برای پیش بینی تولید انرژی خورشیدی
عنوان انگلیسی مقاله: A Hybrid Intelligent System to forecast solar energy production
مجله/کنفرانس: کامپیوترها و مهندسی برق - Computers & Electrical Engineering
رشته های تحصیلی مرتبط: مهندسی انرژی، برق
گرایش های تحصیلی مرتبط: سیستم های انرژی، انرژی های تجدیدپذیر، انتقال و توزیع، هوش ماشین
کلمات کلیدی فارسی: سیستم هوشمند ترکیبی، خوشه بندی، رگرسیون، شبکه های عصبی، انرژی خورشیدی، انرژی های تجدیدپذیر
کلمات کلیدی انگلیسی: Hybrid Intelligent System، Clustering, regression، Neural networks، Solar energy، Renewable energies
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus - Master Journals List - JCR
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.compeleceng.2019.07.023
دانشگاه: Grupo de Inteligencia Computacional Aplicada (GICAP), Departamento de Ingeniería Civil, Escuela Politécnica Superior, Universidad de Burgos, Av. Cantabria s/n, 09006 Burgos, Spain
صفحات مقاله انگلیسی: 15
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2019
ایمپکت فاکتور: 2/762 در سال 2018
شاخص H_index: 49 در سال 2019
شاخص SJR: 0/443 در سال 2018
شناسه ISSN: 0045-7906
شاخص Quartile (چارک): Q2 در سال 2018
فرمت مقاله انگلیسی: PDF
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
آیا این مقاله مدل مفهومی دارد: ندارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: دارد
کد محصول: E13244
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست مطالب (انگلیسی)

Abstract

1- Introduction and previous work

2- Hybrid Intelligent System

3- Case Study: solar energy prediction

4- Results & discussion

5- Conclusions and future work

References

بخشی از مقاله (انگلیسی)

Abstract

There is wide acknowledgement that solar energy is a promising and renewable source of electricity. However, complementary sources are sometimes required, due to its limited capacity, in order to satisfy user demand. A Hybrid Intelligent System (HIS) is proposed in this paper to optimize the range of possible solar energy and power grid combinations. It is designed to predict the energy generated by any given solar thermal system. To do so, the novel HIS is based on local models that implement both supervised learning (artificial neural networks) and unsupervised learning (clustering). These techniques are combined and applied to a real-world installation located in Spain. Alternative models are compared and validated in this case study with data from a whole year. With an optimum parameter fit, the proposed system managed to calculate the solar energy produced by the panel with an error that was lower than 10−4 in 86% of cases.

Introduction and previous work

Renewable Energy (RE) has a key role to play in the field of increased sustainability and is one of the most relevant technologies in that respect [1]. As a consequence, many buildings and especially new ones now incorporate RE facilities. This general European trend has also been applicable to Spain over recent years, where the rate of new RE installations, in general, and solar thermal energy, in particular, is increasing. One reason is found in the Spanish legal regulation on this matter [2], which states that solar system installations are mandatory in new buildings. Obviously, the energy generated by these installations implies a saving on other sources of energy that would otherwise have been used for that purpose. If the energy needs are known in advance, such information may be used to predict the energy thresholds and when the available energy will no longer be demanded. [3]. As a result, it will be possible to take corrective actions with the purpose of reducing the energy from non-renewable energy sources. Various works have addressed that challenge in different ways. In [4], a new strategy was proposed for the optimal scheduling problem, taking into account the impact of uncertainties in wind, solar PV, and load demand forecasts.