Temperature Analysis of the Stand-Alone and Building Integrated Photovoltaic Systems Based on Simulation and Measurement Data

Abstract

Sunlight is converted into electrical energy due to the photovoltaic effect in photovoltaic cells. Energy yield of photovoltaic systems depends on the solar array location, orientation, tilt, tracking and local weather conditions. In order to determine the amount of energy produced in a photovoltaic system, it is important to analyze the operation of the photovoltaic (PV) arrays in real operating conditions and take into account the impact of external factors such as irradiance, ambient temperature or the speed of blowing wind, which is the natural coolant of PV panels. The analysis was carried out based on mathematical models and actual measurement data, regarding the dependence of the average temperature of PV arrays on variable and difficult to predict ambient conditions. The analysis used standard (nominal operating cell temperature (NOCT)), King, Skoplaki, Faiman and Mattei thermal models and the standard model for flat-plate photovoltaic arrays. Photovoltaic installations PV1, PV2a and PV2b, being part of the hybrid power plant of the Bialystok University of Technology, Poland, were the objects of the research. In the case of a free-standing solar system, the Skoplaki model proved to be the best method for determining the average temperatures of the PV arrays. For building-integrated PV systems, a corrected value of the mounting coefficient in the Skoplaki model was proposed, and the original results were compared. The comparison of the accuracy measures of the average operating temperatures for three micro-power plants, differently mounted and located, is presented.

IoT Solutions for Maintenance and Evaluation of Photovoltaic Systems

“…Temperature control is therefore a key factor in improving the performance of a PV system. A detailed analysis of this issue and mathematical models for temperature determination are discussed in detail in the article by A. Idzkowski, K. Karasowska, and W. Walendziuk [55].…”

The Use of Machine Learning Algorithms to Forecast Energy Production in a Small PV System

“…Każdy plik był oddzielny dla danego roku, zaś dane w każdym pliku zawierały pomiary w okresie od 1 kwietnia do 30 września. System pomiarowy rejestruje wartości z okresem około 10 sekund, co przekłada się na ponad 2,1 miliona rekordów dla każdego roku w analizowanym okresie [14]. Konieczne zatem było zredukowanie danych przez określenie temperatury minimalnej oraz maksymalnej, maksymalnego ciśnienia atmosferycznego, średniej prędkości wiatru jak i obliczenie ilości pozyskanej energii dla poszczególnych dni z tego okresu.…”

Current prospects of building-integrated solar PV systems and the application of bifacial PVs

“…For electrical energy yield and thermal performance areas, the studies by Biyik et al (2017), Pandey (2016), and Yu et al (2021) benefit researchers and practitioners working on or interested in BIPV system design, analysis, modeling, performance evaluation, financial development and incentives, and new techniques and trends. Electrical energy yield has interesting areas, such as the papers by Zahedi (2006), Martín-Chivelet et al (2018), Akbari et al (2019), Irshad et al (2019), Idzkowski et al (2020), , Singh et al (2021), Uzum (2021), and Lamnatou et al (2022), which addressed the function of energy storage for PV in the situation of prospect energy storage progresses, provided an assessment between the advantages and disadvantages of the key methods, and examined the resolution techniques which used machine learning, deep learning, and artificial intelligence-based optimization. According to these studies, an increase of 22% in potential energy saving and a reduction of 5 °C-10 °C interior temperature from the ambient surroundings can be achieved.…”