A Review on Artificial Intelligence Applications for Grid-Connected Solar Photovoltaic Systems

Kurukuru, Varaha Satya Bharath; Haque, Ahteshamul; Khan, Mohammed Ali; Sahoo, Subham; Malik, Azra; Blaabjerg, Frede

doi:10.3390/en14154690

Cited by 53 publications

(33 citation statements)

References 197 publications

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“…Despite the holistic view proposed in this article, most of the published systematic literature reviews linked to solar PV have showed a technical focus, covering topics such as: advances in solar cell research and testing [18][19][20][21], energy losses and degradation of PV modules [22][23][24], forecasting of solar photovoltaic radiation and electricity generation [25,26], digital technologies for PV monitoring [27], and leaching of metals from EOL PV waste [28,29]. Other review articles have been more market-oriented, highlighting the need for government interventions in supporting PV diffusion [30]; the factors influencing residential households' adoption of PV systems [31,32]; and descriptions of the current PV market, its associated costs, and available technologies [33].…”

Section: Analytical Framework and Related Literaturementioning

confidence: 99%

A Systematic Literature Review of the Solar Photovoltaic Value Chain for a Circular Economy

Franco

Groesser

2021

Sustainability

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As the solar photovoltaic market booms, so will the volume of photovoltaic (PV) systems entering the waste stream. The same is forecast for lithium-ion batteries from electric vehicles, which at the end of their automotive life can be given a second life by serving as stationary energy storage units for renewable energy sources, including solar PV. The main objective of this paper is to systematically review the “state-of-the-art” research on the solar PV value chain (i.e., from product design to product end-of-life), including its main stages, processes, and stakeholder relationships, in order to identify areas along the value chain where circular strategies could be implemented, thereby advancing the transition of the PV industry towards circularity. To achieve this goal, we conducted a systematic literature review of 148 peer-reviewed articles, published in English between 2000 and 2020. Results show the PV value chain has been studied from a forward flow supply chain perspective and mostly from a technological point of view, with little regard for circular design, circular business models, and PV reuse. This article thus takes an integrated value chain perspective, introduces some of the barriers to circularity that industry players face, and argues that these barriers represent future opportunities for incumbent and new entrants to innovate within a circular PV industry.

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Section: Analytical Framework and Related Literaturementioning

confidence: 99%

A Systematic Literature Review of the Solar Photovoltaic Value Chain for a Circular Economy

Franco

Groesser

2021

Sustainability

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“…Solar plant systems have complex nonlinear dynamics with uncertainties since the system’s parameters and insolation fluctuate [ 1 ]. Thereby, it is complicated to approximate these complex dynamics with classical methods, while ML methods provide the required performance [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

“…Thereby, it is complicated to approximate these complex dynamics with classical methods, while ML methods provide the required performance [ 2 ]. In modern sensor systems, ML methods are crucial units to increase the quality of big dataset processing for solar plant design, forecasting, maintenance, and control [ 1 , 2 ]. Within the EU COVID-19 strategic reply, the smart energy standards define a cloud platform specification for a distributed solar big data ecosystem that will provide the creation of effective ML technologies for smart solar energy [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

A Review on Machine Learning Applications for Solar Plants

Engel

2022

Sensors

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A solar plant system has complex nonlinear dynamics with uncertainties due to variations in system parameters and insolation. Thereby, it is difficult to approximate these complex dynamics with conventional algorithms whereas Machine Learning (ML) methods yield the essential performance required. ML models are key units in recent sensor systems for solar plant design, forecasting, maintenance, and control to provide the best safety, reliability, robustness, and performance as compared to classical methods which are usually employed in the hardware and software of solar plants. Considering this, the goal of our paper is to explore and analyze ML technologies and their advantages and shortcomings as compared to classical methods for the design, forecasting, maintenance, and control of solar plants. In contrast with other review articles, our research briefly summarizes our intelligent, self-adaptive models for sizing, forecasting, maintenance, and control of a solar plant; sets benchmarks for performance comparison of the reviewed ML models for a solar plant’s system; proposes a simple but effective integration scheme of an ML sensor solar plant system’s implementation and outlines its future digital transformation into a smart solar plant based on the integrated cutting-edge technologies; and estimates the impact of ML technologies based on the proposed scheme on a solar plant value chain.

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“…The most important variable in decision making for trading strategies is the photovoltaic power forecast. Reviews of photovoltaic power forecasting methods in [2], [3] conclude that Artificial Neural Networks (ANNs) are the most used artificial intelligence technique in solar power forecasting, as they have a proven track record in terms of accuracy for a variety of situations and with numerous input variables. Decision-makers of the energy sector need to understand how decision-aid tools construct their outputs from the data representing such dynamic multiscale systems.…”

Section: Introduction a Ai Applications In The Energy Sectormentioning

confidence: 99%

Interpretable data-driven solar power plant trading strategies

Parginos

Bessa

Camal

et al. 2022

2022 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe)

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Standard practices of decision-making in energy systems are dynamic, non-linear, complex, and chaotic processes in nature. Trading the power produced by solar photovoltaic (PV) plants in electricity markets is an important decisionmaking problem which receives increasing attention in the past few decades. The main objective of this paper is to build an interpretable data-driven decision aid model for the case study of a solar power plant with the objective to minimize imbalance costs and thus maximise the revenue, using Symbolic Regression (SR) through Genetic Programming. The use of SR in the experiments and analysis developed in this paper show numerous advantages. SR evolves linear combinations of nonlinear functions of the input variables. Three penalty metrics are introduced to enhance the interpretability of the final solutions. SR shows robust results, especially in the case study.

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A Review on Artificial Intelligence Applications for Grid-Connected Solar Photovoltaic Systems

Cited by 53 publications

References 197 publications

A Systematic Literature Review of the Solar Photovoltaic Value Chain for a Circular Economy

A Systematic Literature Review of the Solar Photovoltaic Value Chain for a Circular Economy

A Review on Machine Learning Applications for Solar Plants

Interpretable data-driven solar power plant trading strategies

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