Photovoltaic Energy Production Research Articles

Explainable Spatio-Temporal Graph Neural Networks for multi-site photovoltaic energy production

In recent years, there has been a growing demand for renewable energy sources, which are inherently associated with a decentralized distribution and dependent on weather conditions. Their management and associated forecasting of produced energy are tasks of increasing complexity. Spatio-Temporal Graph Neural Networks have been applied in this context with excellent results, taking advantage of the correct integration of both topological data, defined by the distribution of the plants in the territory, and temporal data of the time series. A drawback of graph neural networks is the recurrent mechanism adopted to process the temporal part, which increases greatly the computational load of these models. Moreover, these models are formulated for real and sensitive contexts where, in addition to being accurate, the predictions must also be understandable by the human operator. For these reasons, in this paper we propose a novel explainable energy forecasting framework based on Spatio-Temporal Graph Neural Networks: the forecasting model generates predictions by processing temporal and spatial information using a spectral graph convolution and a 1D convolutional neural network respectively, then we apply a state-of-the-art explainer to them in order to produce explanations about the generation process. Our proposed method obtains predictions having better performance than previous approaches, both in terms of computational efficiency and prediction accuracy, with the possibility of interpreting them in order to understand the generation process. The novel approach based on fusion of forecasting and explainability in a single framework enables the creation of powerful and reliable systems suitable for real-world issues and challenges.

Development of a low-tech MPC-algorithm for versatile applications in buildings with thermal activated components

Buildings with thermal activated components enable short-term storage of heat and cold with a high level of comfort for the inhabitants. However, a challenge for temperature control is the inertia of the storage mass and the behaviour of conventional control systems during extreme weather events such as long heat periods. Predictive controllers, working with weather forecast data in combination with a simplified building model enable a prediction of the system behaviour in order to manage heating and cooling in an efficient way.Due to the fact, that existing model predictive controller (MPC) follow the principle of higher system intelligence, this work focuses on the most simple design for a MPC. The following scientific study describes the simple respectively low-tech MPC approach, the experimental setup & the simulation in Matlab/Simulink and the comparison with conventional controller solutions. Although the authors use the term low-tech MPC, the proposed solution can be seen as being at the lower bound to a MPC logic and in the classification as a “predictive optimization controller”.The developed algorithm processes solar radiation gains and outside temperature forecasts with building characteristics and calculates a predicted room temperature profile for 48 h into the future. By optimizing the room temperature in relation to the set temperature, a prognosis of the necessary energy demand is possible. The analysis and validation of the algorithm was carried out in a simulated building model with a thermal activated building component (e.g. ceiling/floor-heating). First results show that the developed algorithm is capable of meeting comfort parameters better than conventional controller strategies and therefore useful for various operations in buildings. Possible future extensions can focus on the ideal combination of heating/cooling demand and cost optimization via own PV energy production.

Photovoltaic energy production systems and the carbon market in Maranhao: a case study

The dissertation deals with a research carried out with data from a 168.4kWcc/120kWca photovoltaic solar plant installed in Itapecuru Mirim, in the state of Maranhão, with the objective of modeling a system for parameterization and possible calculation of the effect of the solar photovoltaic system on the market of carbon credit. Throughout the introduction and the theoretical framework, advances in photovoltaic solar technology in the world and in Brazil are presented. The project aims to carry out data collection such as power, voltage, current, yield and performance in a solar farm already obtained in the system and analyze the impact of the efficient use of solar energy. The system used as a base has the following characteristics: plant for distributed generation, in remote self-consumption mode, use of Canadian modules, kUmax line, CS3U-380|385|390|395|400MS, 1000V/1500V, number of boards: 440 units, 22 units of strings of 20 modules. and 2 units of Sungrow SG 60KTL inverters, total area: 0.31h, rated power: 1.36kWp, inverter power: 120Kw. The methodology consists of the following steps: First, discussing the role of the solar photovoltaic system in carbon crediting models, starting with a discussion of this type of system in the global scenario, the role in the global and regional scenario. Then, the impact of solar photovoltaic projects on the avoidance of atmospheric carbon emissions will be evaluated, and based on data obtained from the photovoltaic solar plant in Itapecuru Mirim - MA, a model and visualization of the impact result for the state will be carried out.

Temperature effect in the energy degradation of photovoltaic power system

The modelling of output power for the photovoltaic system is essential for system design and local resource prediction. Accurate photovoltaic power modelling the foremost vital issue is systems efficiency analysis. The temperature plays the main role in the energy degradation of the photovoltaic systems, especially in the host sites. In this paper, experimental and theoreti-cal investigation into the photovoltaic module energy degradation due to temperature effects. This work objectives to investigate the photovoltaic power generated due to the ambient tem-perature effect. The presented results show that the ambient temperature has positive effects on the photovoltaic module energy production during the winter period and negative effects during the summer period. For the proposed photovoltaic system with a capacity of 2.97 kWp the expected theoretical annual energy production by about 554.01 kWh while the annual experiment production was l493.73 kWh. The novelty of the work is to estimate the energy losses due to the ambient temperature effect on the photovoltaic energy production.

Performance Analysis of a Diabatic Compressed Air Energy Storage System Fueled with Green Hydrogen

The integration of an increasing share of Renewable Energy Sources (RES) requires the availability of suitable energy storage systems to improve the grid flexibility and Compressed Air Energy Storage (CAES) systems could be a promising option. In this study, a CO2-free Diabatic CAES system is proposed and analyzed. The plant configuration is derived from a down-scaled version of the McIntosh Diabatic CAES plant, where the natural gas is replaced with green hydrogen, produced on site by a Proton Exchange Membrane electrolyzer powered by a photovoltaic power plant. In this study, the components of the hydrogen production system are sized to maximize the self-consumption share of PV energy generation and the effect of the design parameters on the H2-CAES plant performance are analyzed on a yearly basis. Moreover, a comparison between the use of natural gas and hydrogen in terms of energy consumption and CO2 emissions is discussed. The results show that the proposed hydrogen fueled CAES can effectively match the generation profile and the yearly production of the natural gas fueled plant by using all the PV energy production, while producing zero CO2 emissions.

Experimental optimization of the heating element for a direct-coupled solar photovoltaic water heater

In the last few years, the price of photovoltaic (PV) modules has decreased significantly. As a result, solar PV has become an interesting option for water heating. In a direct-coupled PV water heater (DPVWH) system, the PV array is directly connected to the heating element. Therefore, the optimization of the heating element resistance value is an essential step to design a DPVWH system. This research aims to optimize the resistance value of the heating element for different climatic conditions (Montreal and Bagotville in Canada, and Fez in Morocco). Simulation models were developed using MATLAB/Simulink and TRNSYS software to evaluate the performance of a PV system directly connected with the heating element over a one-year period. This simulation model was validated with an experimental setup. The coefficient of determination (R2) ranges from 98.75% to 99.89%. The results of the experimental optimization indicate that in areas with high incident solar irradiation, the optimal resistance value is low, while the opposite is true for areas with lower solar irradiation. Furthermore, it is found that a direct-coupled PV water heater with a capacity of 3000 Wp achieves an average annual solar fraction of 41% in Montreal, and 73% in Fez. Additionally, the simulation results show that the use of an MPPT (Maximum Power Point Tracking) controller could increase the PV energy production by 29% in average. However, a direct-coupled PV solar water heater system is recommended for a remote and cold region, due to its robustness, its low operating cost, and its simplicity.

Sequential stochastic and bootstrap methods to generate synthetic solar irradiance time series of high temporal resolution based on historical observations

Generating synthetic solar irradiance data through computational methods has become an attractive approach, especially when obtaining accurate measurements from historical observations or satellite-derived estimates is not feasible. Since solar photovoltaic energy production is influenced by variable weather conditions, having reliable solar irradiance data is crucial for evaluating the performance of photovoltaic systems accurately. In this paper, we propose two methods, namely stochastic method, and bootstrap method, to generate one-day synthetic solar irradiance data at a minimum 60-minute time resolution. Both techniques consider dynamic meteorological behavior and maintain the physical significance of the observed data, effectively capturing the intermittent nature of solar irradiance. Validations were performed for five sites with different climate conditions and significant geographical separation, and different time resolutions, through various metrics, including variability, statistical distribution, and energy production, which demonstrated mean average percentage errors as low as 2.1% for intraday irradiance fluctuations, statistical distribution goodness-of-fit up to 80%, and discrepancy for energy production close to 2.4%. results verify the applicability of the proposed methods regardless the time resolution, the location of the measured data and the procedure to record it (i.e., from ground weather stations or satellite-derived estimates). We also evaluated the computational performance of the proposed methods, synthetic, where the elapsed time to generate the synthetic data takes less than a minute to generate a hundred one-day solar irradiance time series. The novelty of the proposed methods is that it is only needed the information of a single month to generate representative synthetic solar irradiance one-day sequences generated for five climate conditions without the need of a training process (i.e., iterative adjustments of the parameters), being useful for pre-feasibility studies of the photovoltaic project and to train machine learning algorithms that deals with time series. To encourage the reproducibility of the research presented in this paper, the proposed methods to generate synthetic solar irradiance data are freely available in a well-documented GitHub repository from https://github.com/salazarna/synthetic-irradiance-sequence.

One-Class Machine Learning Classifiers-Based Multivariate Feature Extraction for Grid-Connected PV Systems Monitoring under Irradiance Variations

In recent years, photovoltaic (PV) energy production has witnessed overwhelming growth, which has inspired the search for more effective operations. Nevertheless, different PV faults may appear, which leads to various degradation stages. Furthermore, under different irradiance levels, these faults may be misclassified as a healthy mode owing to the high resemblances between them, thus provoking serious challenges in terms of power losses and maintenance costs. Hence, interposing the irradiance variation in grid-connected PV (GCPV) systems modeling is important for monitoring tasks to ensure the effective operation of these systems, to increase their reliability and to prevent false alarms. Therefore, in this paper, a fault detection and diagnosis (FDD) method for the GCPV systems using machine learning (ML) based on principal component analysis (PCA) is proposed in order to ensure the reliability and security of the whole system under irradiance variations. The proposed strategy consists of three main steps: (i) introduce the irradiance variations in PV system modeling because of its great impact on power production; (ii) feature extraction and selection through PCA; and (iii) fault classification using ML techniques. In this study, we generate a database that is used to compare the proposed strategy with the standard strategy (considering a fixed irradiance during FDD), to make, at first, a complete and significant comparative assessment of fault diagnosis and to demonstrate the efficiency of the proposed strategy. The achieved results show the high effectiveness of the proposed one-class classification-based approach to detect and diagnose PV array anomalies, reaching an accuracy up to 99.68%.

Water Management Adaptation to Climate Change in Mediterranean Semiarid Regions by Desalination and Photovoltaic Solar Energy, Spain

Integration of renewable energy sources and water production technologies is a must when facing water scarcity problems in semiarid regions, such as Mediterranean regions. The use of additional water resources and production methods, such as reclaimed water and, more specifically, desalinated water, means present and necessary water resources to introduce in the water balances to attend to water demands within a global warming and droughting scenario. These solutions have the inconvenience of energy/power needs and costs. However, the development of renewable energies like photovoltaic solar energy, with lower and lower costs and greater efficiency, makes these economically feasible facilities, reaching competitive production costs for marine or sea desalinated water by around 50% of reduction in energy costs and 20–30% of savings in final water production cost. This paper presents a practical project or action focused on the integration of renewable energies and new water resources by introducing a Photovoltaic Energy Plant (PVEP) as an energy source to feed a Seawater Desalination Treatment Plant (SWDTP). The PV facility is designed to cover all the energy demanded using the SWDTP during the day, and even studying the possibility of selling the energy production exceeds and injecting them into the energy supply network, covering the needs of buying energy needed during the high period where there is no photovoltaic energy production. Thus, savings related to energy costs and even incomes coming from energy sales mean an important reduction in operation costs or expenditures (OPEX), which makes economically feasible and sustainable the investment and the final price of water produced within the Mutxamel SWDTP. The final reduction cost in water desalination reaches 25% on average.

Intraseasonal and synoptic modulation of diurnal surface solar radiation over Reunion island in the South-West Indian Ocean

Understanding the space-time variability of Surface Solar Radiation (SSR) is mandatory for the prediction and, eventually, the skillful forecasting of photovoltaic energy production. This paper addresses the modulation of local-scale SSR over Reunion, a tropical island in the South-West Indian Ocean, by the leading modes of climate variability influencing both regional-scale and local-scale atmospheric convection and its associated cloud cover. Analyses focus on synoptic (tropical cyclones [TCs], synoptic convective regimes, including Tropical-Temperate Troughs [TTTs]) and intraseasonal (Madden-Julian Oscillation [MJO]) timescales. The SSR intra-daily variability is first assessed by a diurnal classification of SARAH-E satellite SSR data, and it is then related to the climate conditions mentioned above. SSR anomalies are found larger (smaller) on the windward (leeward) side of Reunion and in the summer (winter) season. The island-scale “cloudy” conditions can typically last 1 or 2 days. Nearby TCs can strongly reduce SSR by up to 50% on average, depending on their distances from Reunion, their sizes, and particularly, their longitudinal positions, which is observed for the first time. Nearby TCs are associated with significant negative SSR anomaly when located west of Reunion but with less significant or even positive anomaly when located east of the island. Synoptic convective regimes (the intraseasonal MJO) have a relatively weaker impact on SSR, with a value up to 13% (5%) of the mean value. Potential interactions between these SSR modulators are also investigated to understand better and eventually predict the mechanisms likely to modulate SSR (and thus photovoltaic electricity production) at sub-seasonal timescales.

Evaluating the impact of wildfire smoke on solar photovoltaic production

There are growing needs to understand how extreme weather events impact the electrical grid. Renewable energy sources such as solar photovoltaics are expanding in use to help sustainably meet electricity demands. Wildfires and, notably, the widespread smoke resulting from them, are one such extreme event that can impair the performance of solar photovoltaics. However, isolating the impact that smoke has on photovoltaic energy production, separate from ambient conditions, can be difficult. In this work, we seek to understand and quantify the impacts of wildfire smoke on solar photovoltaic production within the Western United States. Our analysis focuses on the construction of a random forest regression model to predict overall solar photovoltaic production. The model is used to separate and quantify the impacts of wildfire smoke in particular. To do so, we fuse historical weather, solar photovoltaic energy production, and PM2.5 particulate matter (primary smoke pollutant) data to train and test our model. The additional weather data allows us to capture interactions between wildfire smoke and other ambient conditions, as well as to create a more powerful predictive model capable of better quantifying the impacts of wildfire smoke on its own. We find that solar PV energy production decreases 8.3% on average during high smoke days at PV sites as compared to similar conditions without smoke present. This work allows us to improve our understanding of the potential impact on photovoltaic-based energy production estimates due to wildfire events and can help inform grid and operational planning as solar photovoltaic penetration levels continue to grow.

A novel transactive integration system for solar renewable energy into smart homes and landscape design: A digital twin simulation case study

The use of solar renewable energy in landscape design is becoming increasingly popular as the world strives to reduce its reliance on fossil fuels. With the cost of solar energy falling, more and more people are turning to solar as a way to power their homes and businesses. In this article, we will explore the integration of solar renewable energy into landscape design through a case study. Using local photovoltaics along with suitable power storage systems, a household power management system is proposed to optimize the scheduling of demand-responsive appliances. The beta likelihood distribution function for sun irradiance is used to model the unpredictable behavior of photovoltaic energy production in residential power management systems. The PV system was integrated into the building’s existing landscape design by incorporating the system’s components into the existing landscape elements. This included the installation of a large solar array on the roof of the building, as well as the integration of solar panels into the existing garden beds and pathways. The solar array was also connected to the building’s existing electrical system, allowing the building to draw power from the solar array when needed. Among the major contributions of the study would be to optimize the planning of demand-responsive devices within a domestic power management system by using the improved leader particle swarm optimization method (ILPSO). The purpose would be to reduce peak-to-average ratios (PARs) and energy usage costs in the smart home. A number of scenarios are designed and simulated in a digital twin structure for a household user with various initial loads, uninterruptible deferrable, and interruptible deferrable devices using a real-time power cost program to demonstrate the performance of the suggested optimization method. Based on comparisons with various metaheuristics previously documented, the new ILPSO algorithm efficiently optimizes energy usage costs and PARs.

Impacts of economic regulation on photovoltaic distributed generation with battery energy storage systems

Photovoltaic systems are largely involved in the process of decarbonization of the electricity production. Among the solutions of interest for deploying higher amounts of photovoltaic (PV) energy generation for reducing the electricity taken from the grid, the inclusion of local battery energy storage systems has been considered. Battery energy storage provides an energy buffer useful to better manage the fluctuations of PV energy production, or to serve the demand when the PV generation is absent or insufficient and the price of the electricity taken from the grid is high. While technically sound, the installation of a PV system with battery energy storage has to demonstrate its profitability in the specific context of application, also depending on the regulation in place in the relevant jurisdiction. This paper presents the stochastic economic feasibility analysis for the installation of distributed photovoltaic power plants facing the new Brazilian regulation of electric energy compensation system, and also considers the hourly tariff known as White Tariff. Three different sizes of distributed power plants are proposed, and the related models introduce battery banks to regulate the peak demand when tariffs are more expensive. In the absence of economic incentive policies to support this kind of renewable energy generation associated with battery energy storage systems, there is a lower probability of economic viability, especially for micro-plants up to 10 kW of installed power.

A comparison study of grid impact of photovoltaic installations in Brazil according to Normative Resolution 482 and Federal law 14.300

This work focuses on comparing the Brazilian first distributed energy net-metering policy, instituted by Normative Resolution (NR) nº 482/2012, and the new system, instituted by Federal Law 14,300/2022. A review of the development of international distributed energy regulation is presented, along with a review of the impacts caused to distribution grids because of distributed energy systems. A set of equations is presented to compare the energy billing for consumers under the old and the new Brazilian policies. Using the indication of the amount of energy the consumers are inclined to produce to reduce their bill, the PV energy production scenarios for five different low voltage distribution grids in Brazil are simulated. The simulations indicate that an increase in the power injection into the Brazilian distribution grids could lead to transformer overload and end-of- line overvoltage. The investment cost and loss of revenues from the installation of PV systems are also compared between different regulations scenarios, showing better revenues but also greater investment required for utilities under the new regulation. One of the main conclusions is that new Brazilian policy is reducing the loss of revenue in distributed energy, however, there is a risk for misplaced investments in the distribution grid.

Load management, energy economics, and environmental protection nexus considering PV-based EV charging stations

Integrating electric vehicles (EVs) into the electric power system poses significant challenges to grid operation and planning due to the potential constraints on the power system. To minimize grid congestion, the best option is to increase the production of photovoltaic (PV) energy for domestic use and EV charging stations. This paper addresses the nexus of technical, financial, and environmental effects of customer involvement in economic development and load management. In this paper, a new model design of solar-powered EV charging stations is proposed and implemented in HOMER Grid, and a case study has explored how economic, technical, and energy management benefits can be achieved through customer energy involvement and the integration of PV-based charging stations. The proposed PV-based charging stations contribute toward the energy management of the region, and the study observes the real-time optimal charging and discharging strategy of PV-based grid-connected charging stations. The case study results show that the investigated area can produce 1,070,804.096 MWh/year of energy through maximum customer involvement, consequently reducing overall energy costs. Moreover, the study demonstrates that the selected region can produce 86,961,688 kWh/year through the PV system, achieving 363,899 charging sessions per year, offering maximum advantages and facilitation for EV charging. The proposed model applicability shows that large-scale customer involvement can bring significant techno-economic benefits and attract regional investments.

Experimental Investigation and Modelling of Sediments Effect on the Performance of Cadmium Telluride Photovoltaic Panels

Of the different renewable sources of energy, photovoltaic energy has one of the highest potentials. In recent decades, several technological and research advances have contributed to the consolidation of its potential. One current photovoltaic energy research topic is the analysis of the impact of sediments on the panels’ performance. The development of models to predict the performance of panels in the presence of sediments may allow for better decision-making when considering maintenance operations. This work contributed to the investigation of the influence of sand on the production of photovoltaic energy in cadmium telluride (CdTe) panels. Six panels of this type with different colors and transparencies were experimentally tested with and without the presence of sand. The impact of the sand on the cells’ performance was evaluated by analyzing the change in the 1M5P model’s parameters and in the power, efficiency, and fill factors. The experimental results show different negative impacts on the output power of the CdTe panels, from −14% in the orange panel to −36% in the green panel. Based on this study, the development of a model capable of predicting the effect of the sand on these panels was introduced. The developed model was validated experimentally, with a maximum deviation of 4.6%. These results can provide support for the decision-making around maintenance activities and for the development of new techniques to avoid sediment deposition on CdTe panels.

Assessing the national synergy potential of onshore and offshore renewable energy from the perspective of resources dynamic and complementarity

Intermittent renewable energy sources have been transformed from supplementary energy to dominant incremental alternative energy sources in the context of carbon neutrality target worldwide. For coastal countries, it is urgent to integrate abundant offshore energy into regional onshore renewable power sources economically and reliably considering the scarcity of land resources and negative environmental impacts. However, limited knowledge on the complex spatiotemporally synergy relationships between energy potential (onshore and offshore) is a barrier in making reasonable policies to promote collaborative development of onshore and offshore energies. This study presents a spatiotemporally explicit modelling framework for assessing the synergy potential characteristics of onshore and offshore renewable energy sources. The framework firstly estimates the technical potential of solar PV and wind energy across the country by using 40 years of hourly meteorological reanalysis data (1980–2020), and then investigates the long-term changes trend, stability, and complementarity of solar-wind energy potential spatiotemporally at both onshore and offshore sub-regions. We find that a considerable amount of solar PV and wind energy sources could be harnessed in China, and onshore potential across the country (178 PWh/yr) is approximate 20 times higher than offshore energy potential (8.9 PWh/yr). Overall, onshore solar-wind energy potentials exhibit statistically insignificant but slightly increasing trend over the past 40 years, while offshore wind potentials observe an opposite change trend for solar PV and wind energy production. Complementarity assessment showed the northeast and offshore parts of China present high complementarity effects for hourly and daily power production of solar-wind systems. Furthermore, inter-regional combination between offshore and onshore northwest regions also exhibits promising synergy effect for renewable energy power output. The results on resources dynamic and complementarity highlight the advantage of interregional resources cooperative configuration and electric power grid interconnections. The analysis performed in this paper could provide a global picture for the integration planning and collaborative development strategies formulation of on-/offshore renewable energy across the country.

Rooftop Photovoltaic Energy Production Estimations in India Using Remotely Sensed Data and Methods

We investigate the possibility of estimating global horizontal irradiance (GHI) in parallel to photovoltaic (PV) power production in India using a radiative transfer model (RTM) called libRadtran fed with satellite information on the cloud and aerosol conditions. For the assessment of PV energy production, we exploited one year’s (January–December 2018) ground-based real-time measurements of solar irradiation GHI via silicon irradiance sensors (Si sensor), along with cloud optical thickness (COT). The data used in this method was taken from two different sources, which are EUMETSAT’s Meteosat Second Generation (MSG) and aerosol optical depth (AOD) from Copernicus Atmospheric Monitoring Services (CAMS). The COT and AOD are used as the main input parameters to the RTM along with other ones (such as solar zenith angle, Ångström exponent, single scattering albedo, etc.) in order to simulate the GHI under all sky, clear (no clouds), and clear-clean (no clouds and no aerosols) conditions. This enabled us to quantify the cloud modification factor (CMF) and aerosol modification factor (AMF), respectively. Subsequently, the whole simulation is compared with the actual recorded data at four solar power plants, i.e., Kazaria Thanagazi, Kazaria Ceramics, Chopanki, and Bhiwadi in the Alwar district of Rajasthan state, India. The maximum monthly average attenuation due to the clouds and aerosols are 24.4% and 11.3%, respectively. The energy and economic impact of clouds and aerosols are presented in terms of energy loss (EL) and financial loss (FL). We found that the maximum EL in the year 2018 due to clouds and aerosols were 458 kWh m−2 and 230 kWh m−2, respectively, observed at Thanagazi location. The results of this study highlight the capabilities of Earth observations (EO), in terms not only of accuracy but also resolution, in precise quantification of atmospheric effect parameters. Simulations of PV energy production using EO data and techniques are therefore useful for real-time estimates of PV energy outputs and can improve energy management and production inspection. Success in such important venture, energy management, and production inspections will become much easier and more effective.

A user-friendly and accurate machine learning tool for the evaluation of the worldwide yearly photovoltaic electricity production

While traditional methods for modelling the thermal and electrical behaviour of photovoltaic (PV) modules rely on analytical and empirical techniques, machine learning is gaining interest as a way to reduce the time, expertise, and tools required by designers or experts while maintaining high accuracy and reliability. This research presents a data-driven machine learning tool based on artificial neural networks (ANNs) that can forecast yearly PV electricity directly at the optimal PV inclination angle without geographic restrictions and is valid for a wide range of electrical characteristics of PV modules. Additionally, empirical correlations were developed to easily determine the optimal PV inclination angle worldwide. The ANN algorithm, developed in Matlab, systematically and quantitatively summarizes the behaviour of eight PV modules in 48 worldwide climatic conditions. The algorithm’s applicability and robustness were proven by considering two different PV modules in the same 48 locations. Yearly climatic variables and electrical/thermal PV module parameters serve as input training data. The yearly PV electricity is derived using dynamic simulations in the TRNSYS environment, which is a simulation program primarily and extensively used in the fields of renewable energy engineering and building simulation for passive as well as active solar design. Multiple performance metrics validate that the ANN-based machine learning tool demonstrates high reliability and accuracy in the PV energy production forecasting for all weather conditions and PV module characteristics. In particular, by using 20 neurons, the highest value of R-square of 0.9797 and the lowest values of the root mean square error and coefficient of variance of 14.67 kWh and 3.8%, respectively, were obtained in the training phase. This high accuracy was confirmed in the ANN validation phase considering other PV modules. An R-square of 0.9218 and values of the root mean square error and coefficient of variance of 31.95 kWh and 7.8%, respectively, were obtained.The results demonstrate the algorithm’s vast potential to enhance the worldwide diffusion and economic growth of solar energy, aligned with the seventh sustainable development goal.

A coupled model of global energy production and ERoEI applied to photovoltaic and wind, an estimation of net production

Photovoltaic (PV) and wind energy production modelling essentially follows bottom-up approaches, based on physical considerations. These models focus mostly on the calculation of Energy Return on Energy Invested (ERoEI), while some also present calculation of energy payback time or energy cannibalism, to evaluate the ability of these technologies to handle energy transition. Besides, sigmoïd or S-curve function are often used to fit the production of these industries over time, based on economical considerations, rather than physical ones. This paper aims at coupling net production and ERoEI, using energy conservation and ERoEI definition, to develop a model that could reproduce a S-curve function and allow to investigate the net energy delivered by PV and wind industries. This approach allows to explain the inconsistency which sometimes exists between bottom-up and top-down studies on PV ERoEI calculation. It also explains why the classic relationship between net and gross energy, that is a ratio equal to (ERoEI−1)/ERoEI, does not apply to low carbon energy industries before they reach a steady-state or a quasi-static evolution. As an example, net PV production is still close to zero whereas wind production began to produce a net substantial energy around 2007–2010. It finally allows to investigate how fast the energy transition can physically be to see if a risk of energy disruption exists during that transition, due to fossil fuel depletion.