To mitigate the impact of climate change and global warming, the use of renewable energies is increasing day by day significantly. A considerable amount of electricity is generated from renewable energy sources since the last decade. Among the potential renewable energies, photovoltaic (PV) has experienced enormous growth in electricity generation. A large number of PV systems have been installed in on-grid and off-grid systems in the last few years. The number of PV systems will increase rapidly in the future due to the policies of the government and international organizations, and the advantages of PV technology. However, the variability of PV power generation creates different negative impacts on the electric grid system, such as the stability, reliability, and planning of the operation, aside from the economic benefits. Therefore, accurate forecasting of PV power generation is significantly important to stabilize and secure grid operation and promote large-scale PV power integration. A good number of research has been conducted to forecast PV power generation in different perspectives. This paper made a comprehensive and systematic review of the direct forecasting of PV power generation. The importance of the correlation of the input-output data and the preprocessing of model input data are discussed. This review covers the performance analysis of several PV power forecasting models based on different classifications. The critical analysis of recent works, including statistical and machine-learning models based on historical data, is also presented. Moreover, the strengths and weaknesses of the different forecasting models, including hybrid models, and performance matrices in evaluating the forecasting model, are considered in this research. In addition, the potential benefits of model optimization are also discussed.

Forecasting of photovoltaic power generation and model optimization: A review

Application of support vector machine models for forecasting solar and wind energy resources: A review

In the last four decades, greater attention has been paid to PV/T systems due to their advantages compared with PV or solar thermal systems alone. This paper aims to study various aspects of PV/T systems through the existing literature in order to highlight key points as future work in this field as well as illustrate different techniques used for such systems. In addition, PV/T systems are reviewed in terms of thermal and electrical side views. Furthermore, the analysis of solar thermal systems, various system applications such as air, water, air/water, phase change material PCM and Nanofluid systems are summarized. In light of most attempts to improve the PV/T system, more focus has been paid to the thermal rather than the electrical side. Furthermore, comparisons between PV/T systems in terms of performance parameters and efficiencies are presented. A critical review of many findings of previously conducted research is also discussed. It is found that the PV/T air heater system is promising for future preheating air applications. Moreover, it is suggested that the use of nanoparticles and water as base fluid improves the overall system efficiency. Furthermore, the PV side views require more attention in technical and cost terms. However, more research is essential to reduce the cost and, improve the effectiveness and technical design of such systems.

Photovoltaic/Thermal (PV/T) systems: Status and future prospects

The modernisation of the world has significantly reduced the prime sources of energy such as coal, diesel and gas. Thus, alternative energy sources based on renewable energy have been a major focus nowadays to meet the world's energy demand and at the same time to reduce global warming. Among these energy sources, solar energy is a major source of alternative energy that is used to generate electricity through photovoltaic (PV) system. However, the performance of the power generated is highly sensitive on climate and seasonal factors. The unpredictable behaviour of the climate affects the power output and causes an unfavourable impact on the stability, reliability and operation of the grid. Thus an accurate forecasting of PV output is a crucial requirement to ensure the stability and reliability of the grid. This study provides a systematic and critical review on the methods used to forecast PV power output with main focus on the metaheuristic and machine learning methods. Advantages and disadvantages of each method are summarised, based on historical data along with forecasting horizons and input parameters. Finally, a comprehensive comparison between machine learning and metaheuristic methods is compiled to assist researchers in choosing the best forecasting technique for future research.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-rpg.2018.5649

Abdullah-Al-Mamoon

Papers

An experimental investigation on performance analysis of air type photovoltaic thermal collector system integrated with cooling fins design

Application of support vector machine for prediction of electrical and thermal performance in PV/T system