Maximum energy yield of PV surfaces in France and Italy from climate based equations for optimum tilt at different azimuth angles

Memme, Samuele; Fossa, Marco

doi:10.1016/j.renene.2022.10.019

Cited by 12 publications

(3 citation statements)

References 36 publications

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“…Generally, the efficiency of solar cells decreases as temperature increases, which can lead to a reduction in electricity production [12]. In cases where precise data for the tilt and azimuth angles of individual installations are unknown, horizontal components of irradiance, which assume no inclination, are often considered as a simplified approach [13]. Therefore, a global horizontal irradiance ghi, as the sum of direct and diffuse irradiances is the most commonly used outcome of the weather forecast, and it will be used in this work as the main input data, as described in the following.…”

Section: Analysis Of the Problemmentioning

confidence: 99%

Machine Learning Models for Regional Photovoltaic Power Generation Forecasting with Limited Plant-Specific Data

Tucci,

Piazzi,

Thomopulos

2024

Energies

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Predicting electricity production from renewable energy sources, such as solar photovoltaic installations, is crucial for effective grid management and energy planning in the transition towards a sustainable future. This study proposes machine learning approaches for predicting electricity production from solar photovoltaic installations at a regional level in Italy, not using data on individual installations. Addressing the challenge of diverse data availability between pinpoint meteorological inputs and aggregated power data for entire regions, we propose leveraging meteorological data from the centroid of each Italian province within each region. Particular attention is given to the selection of the best input features, which leads to augmenting the input with 1-hour-lagged meteorological data and previous-hour power data. Several ML approaches were compared and examined, optimizing the hyperparameters through five-fold cross-validation. The hourly predictions encompass a time horizon ranging from 1 to 24 h. Among tested methods, Kernel Ridge Regression and Random Forest Regression emerge as the most effective models for our specific application. We also performed experiments to assess how frequently the models should be retrained and how frequently the hyperparameters should be optimized in order to comprise between accuracy and computational costs. Our results indicate that once trained, the model can provide accurate predictions for extended periods without frequent retraining, highlighting its long-term reliability.

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Section: Analysis Of the Problemmentioning

confidence: 99%

Machine Learning Models for Regional Photovoltaic Power Generation Forecasting with Limited Plant-Specific Data

Tucci,

Piazzi,

Thomopulos

2024

Energies

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“…A filter is also applied to consider a minimum rooftop area and width and to remove the small rooftop spaces [38]. For the centroid of each of the remaining surfaces, the global irradiance is applied for all combinations of azimuth and tilt using the Liu and Jordan isotropic irradiance model [39], which is widely employed in urban areas due to its accuracy and simplicity [40,41]. The shadows cast by nearby obstacles and buildings are considered to calculate the skyline of surrounding obstacles, as detailed in a previous work [28,42,43].…”

Section: Techno-economic Modelmentioning

confidence: 99%

Techno-Economic Potential of Urban Photovoltaics: Comparison of Net Billing and Net Metering in a Mediterranean Municipality

Fuster-Palop¹,

Prades-Gil²,

Masip³

et al. 2023

Energies

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Solar photovoltaic self-consumption is an attractive approach to increase autarky and reduce emissions in the building sector. However, a successful deployment in urban rooftops requires both accurate and low-computational-cost methods to estimate the self-consumption potential and economic feasibility, which is especially scarce in the literature on net billing schemes. In the first part of this study, a bottom-up GIS-based techno-economic model has helped compare the self-consumption potential with net metering and net billing in a Mediterranean municipality of Spain, with 3734 buildings in total. The capacity was optimized according to load profiles obtained from aggregated real measurements. Multiple load profile scenarios were assessed, revealing that the potential self-sufficiency of the municipality ranges between 21.9% and 42.5%. In the second part of the study, simplified regression-based models were developed to estimate the self-sufficiency, self-consumption, economic payback and internal rate of return at a building scale, providing nRMSE values of 3.9%, 3.1%, 10.0% and 1.5%, respectively. One of the predictors with a high correlation in the regressions is a novel coefficient that measures the alignment between the load and the hours with higher irradiance. The developed correlations can be employed for any other economic or demand scenario.

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“…In this analysis, the focus is on c-Si modules, specifically the Canadian Solar 300 Watt Solar Module, which is assumed to be inclined at a tilt angle of 28 degrees and oriented towards the north. This tilt angle is chosen to be close to both the optimal angle for maximizing yearly energy yield and the typical slope of roofs in the Sydney area [87]. The technical specifications of the Canadian Solar 300 Watt Solar Module are summarized in Table 2.…”

Section: The Sandia Array Performance Model (Sapm)mentioning

confidence: 99%

Assessing the combined effects of local climate and mounting configuration on the electrical and thermal performance of photovoltaic systems. Application to the greater Sydney area

Boccalatte,

Thebault,

Paolini

et al. 2023

Renewable Energy

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Maximum energy yield of PV surfaces in France and Italy from climate based equations for optimum tilt at different azimuth angles

Cited by 12 publications

References 36 publications

Machine Learning Models for Regional Photovoltaic Power Generation Forecasting with Limited Plant-Specific Data

Machine Learning Models for Regional Photovoltaic Power Generation Forecasting with Limited Plant-Specific Data

Techno-Economic Potential of Urban Photovoltaics: Comparison of Net Billing and Net Metering in a Mediterranean Municipality

Assessing the combined effects of local climate and mounting configuration on the electrical and thermal performance of photovoltaic systems. Application to the greater Sydney area

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