Publication library
Journal articles
M., David, J., Alonso-Montesinos, J., Le Gal La Salle, P., Lauret (2023). Probabilistic Solar Forecasts as a Binary Event Using a Sky Camera. Energies, 16, 7125. https://doi.org/10.3390/en16207125
Abstract
With the fast increase of solar energy plants, a high-quality short-term forecast is required to smoothly integrate their production in the electricity grids. Usually, forecasting systems predict the future solar energy as a continuous variable. But for particular applications, such as concentrated solar plants with tracking devices, the operator needs to anticipate the achievement of a solar irradiance threshold to start or stop their system. In this case, binary forecasts are more relevant. Moreover, while most forecasting systems are deterministic, the probabilistic approach provides additional information about their inherent uncertainty that is essential for decision-making. The objective of this work is to propose a methodology to generate probabilistic solar forecasts as a binary event for very short-term horizons between 1 and 30 min. Among the various techniques developed to predict the solar potential for the next few minutes, sky imagery is one of the most promising. Therefore, we propose in this work to combine a state-of-the-art model based on a sky camera and a discrete choice model to predict the probability of an irradiance threshold suitable for plant operators. Two well-known parametric discrete choice models, logit and probit models, and a machine learning technique, random forest, were tested to post-process the deterministic forecast derived from sky images. All three models significantly improve the quality of the original deterministic forecast. However, random forest gives the best results and especially provides reliable probability predictions.
P., Lauret, R., Alonso-Suárez, R., Amaro E Silva, J., Boland, M., David, W., Herzberg, J., Le Gall La Salle, E., Lorenz, L., Visser, W., Van Sark, W., & T, Zech (2024). The added value of combining solar irradiance data and forecasts: A probabilistic benchmarking exercise. Renewable Energy, 237, 121574. https://doi.org/10.1016/j.renene.2024.121574
Abstract
Despite the growing awareness in academia and industry of the importance of solar probabilistic forecasting for further enhancing the integration of variable photovoltaic power generation into electrical power grids, there is still no benchmark study comparing a wide range of solar probabilistic methods across various local climates. Having identified this research gap, experts involved in the activities of IEA PVPS T16 agreed to establish a benchmarking exercise to evaluate the quality of intra-hour and intra-day probabilistic irradiance forecasts.
The tested forecasting methodologies are based on different input data including ground measurements, satellite-based forecasts and Numerical Weather Predictions (NWP), and different statistical methods are employed to generate probabilistic forecasts from these. The exercise highlights different forecast quality depending on the method used, and more importantly, on the input data fed into the models.
In particular, the benchmarking procedure reveals that the association of a point forecast that blends ground, satellite and NWP data with a statistical technique generates high-quality probabilistic forecasts. Therefore, in a subsequent step, an additional investigation was conducted to assess the added value of such a blended point forecast on forecast quality. Three new statistical methods were implemented using the blended point forecast as input.
T. A., Randrianantenaina, J. Le Gal La Salle, S. V., Spataru, & M., David, M. (2025). Increasing the self-sufficiency of a university campus by expanding the PV capacity while minimizing the energy cost. EPJ Photovoltaics, 16, 7. https://doi.org/10.1051/epjpv/2024048
Abstract
Microgrids, which promote the production and consumption of renewable energy on site, are a relevant solution to reduce carbon emissions and the price of energy for end users. However, converting an existing building stock into a microgrid powered mainly by renewable energy requires finding a technical and economic optimum while taking into account strong constraints. This work proposes a methodology to achieve this objective on an existing university campus located in La Reunion, a French island in the Indian Ocean. The campus already has three photovoltaic (PV) systems and high-quality measurement data of weather, loads and energy production. The goal of the work is to find an optimal rooftop PV capacity that maximizes campus selfsufficiency while keeping energy price affordable for users. The results do not highlight a unique combination of roofs as a solution to the optimization problem. However, the analysis of possible combinations gives clear rules for defining the total photovoltaic capacity to be installed and selecting the most suitable roofs.
Le Gal La Salle, J., David, M., Lauret, P., & Castaing-Lasvignottes, J. (2025). Concevoir et dimensionner des microréseaux autonomes: L’exemple de TwInSolar. Innovations technologiques. https://doi.org/10.51257/a-v1-in199
Abstract
Several studies suggest that energy networks, which are currently mainly centralized, could be more efficient through a wider integration of microgrids. Questions then arise about their optimal design. The PIMENT laboratory of the university of La Reunion created a new decision support software dedicated to the design of microgrids, that optimally sizes the key components according to specified performance indicators. This stochastic algorithm is based on the genetic algorithm approach. The results of a study case are deeply studied.
Le Gal La Salle, J., David, M., Lauret, P (2025). A Set of New Tools to Measure the Effective Value of Probabilistic Forecasts of Continuous Variables. Forecasting, 7, 2. https://doi.org/10.3390/forecast7020030
Abstract
In recent years, the prominence of probabilistic forecasting has risen among numerous research fields (finance, meteorology, banking, etc.). Best practices on using such forecasts are, however, neither well explained nor well understood. The question of the benefits derived from these forecasts is of primary interest, especially for the industrial sector. A sound methodology already exists to evaluate the value of probabilistic forecasts of binary events. In this paper, we introduce a comprehensive methodology for assessing the value of probabilistic forecasts of continuous variables, which is valid for a specific class of problems where the cost functions are piecewise linear. The proposed methodology is based on a set of visual diagnostic tools. In particular, we propose a new diagram called EVC (“Effective economic Value of a forecast of Continuous variable”) which provides the effective value of a forecast. Using simple case studies, we show that the value of probabilistic forecasts of continuous variables is strongly dependent on a key variable that we call the risk ratio. It leads to a quantitative metric of a value called the OEV (“Overall Effective Value”). The preliminary results suggest that typical OEVs demonstrate the benefits of probabilistic forecasting over a deterministic approach.
Abstract
In this work, we develop simple linear models that allow users to predict solar irradiance forecast errors based solely on solar variability at a specific location on Earth. These straightforward yet actionable models enable solar forecasters to quickly estimate forecast errors for a given site, providing a clear indication of how well their forecasting models are likely to perform. The error in deterministic solar irradiance forecasts is measured by the Root Mean Square Error (RMSE), while solar variability is quantified by the standard deviation of an hourly time series of changes in the dimensionless clear sky index.
Sixty sites distributed around the globe are used to build two types of RMSE prediction models. The first type is for intra-day forecasts (1-hour to 6-hour forecast horizons), while the second is for day-ahead forecasts (24-hour horizon). The derivation of the intra-day forecast error prediction model leverages on a non-linear time series approach whereas the one for day-ahead forecast error relies on forecasts issued by the European Centre for Medium-Range Weather Forecasts (ECMWF). For each type of model, we calculate also the 2.5% and 97.5% percentiles of the distribution in order to estimate the 95% uncertainty interval associated with the prediction. This uncertainty interval defines the bounds of the RMSE within which 95% of future RMSE values are expected to fall. These error bounds can provide solar forecasters with valuable insights into the performance of their solar forecasting methods in relation to the forecast challenges posed by site-specific variability.
Verification against published results in the literature, specifically for seven sites of the SURFRAD network, demonstrates that these models can satisfactorily predict intra-day and day-ahead forecast RMSEs using only site-specific solar variability data.
Conferences
M., David, M.N. Andriamandroso, M. N., P., Behrensdorff Poulsen, J., Castaing-Lasvignottes, N., Cutululis, K., Das, C., Durif-Aboukali, J., Francou, P., Lauret, J., Le Gal La Salle, E., Lorenz, O., Marc, D., Melgar and S., Spataru (2023). A set of study cases for the massive integration of solar renewables in non-interconnected areas. SWC 2023: ISES Solar World Congress 2023, New-Delhi, 30 Oct. – 4 Nov. https://doi.org/10.18086/swc.2023.05.02
Abstract
E., Lorenz, T., Zech, W., Herzberg, P., Lauret, M., David (2024). Probabilistische Kurzfristvorhersage der Globalstrahlung mittels Analog Ensemble unter Nutzung von satellitenbasierter Einstrahlung. Fachtagung Energiemeteorologie, Bad Staffelstein, Germany, 24 Jan..
Abstract
J., Le Gal La Salle, M., David, P., Lauret (2024). Finding the Optimal Size and Design of a Microgrid Energy System Using Genetic Algorithm. EU PVSEC 2024, Vienna, Austria, 23-27 Sept. 2024.
Abstract
T. A., Randrianantenaina, J., Le Gal La Salle, S. V., Spataru, M., David (2024). Increasing the self-sufficiency of the Terre Sainte campus microgrid by expanding the PV capacity while minimizing the cost. EU PVSEC 2024, Vienna, Austria, 23-27 Sept. 2024.
Abstract
This work is dedicated to enhance the self-sufficiency of the the Terre Sainte campus of the University of La Reunion with onsite solar energy production. The main goal is to boost energy production by integrating additional photovoltaic (PV) panels while minimizing the installation and operation costs. To navigate toward this objective, four distinct tasks have been outlined. The first focus of the study is on validating the collected data before its application. Five thorough quality check tests have been carefully performed. This work clearly explains the significant importance of each test in ensuring the reliability of the dataset. Moving forward, the study dives into the simulation of the current microgrid PV systems, drawing comparisons with recorded data to assess the accurracy and reliability of the model. This step is important in establishing the performance of the simulation tool and its alignment with actual observations. In the next step, the research work provides a comprehensive exploration of the available rooftop areas on the campus, strategically identifying potential expanses for the scaling up of the microgrid PV capacity. By analyzing these available areas, the study lays the foundation for informed decision-making in the pursuit of an optimized and efficient solar energy system. Finally, using the results of the previous tasks, this study focuses on minimizing the Levelized Cost of Energy for self-consumption (LCOEsc) while maximizing the self-sufficiency. This strategic approach aims to help identify the optimal combinations of rooftops suitable for installing the additional PV panels.
Grondin, E., Grondin, D., Delsaut, M., Tang, C., & Morel, B. (2024, November 13). Inter-comparison and validation against in-situ measurments of satellite estimates of incoming solar radiation at Reunion BSRN site. Southern African Sustainable Energy Conference SASEC 2024, Somerset West, South Africa.
Abstract
Lange, J., Thome, R., Castaing-Lasvignottes J., David, M.. (2025, April 14-17). Modelling of a PV collective domestic hot water system. TRANSFERTS 2025, Saint-Pierre, La Reunion.
Abstract
Since 2010, Domestic Hot Water (DHW), must be provided by solar energy in France. Many solutions are available for individual housing to comply with this regulation. However, collective thermal systems are complex and have high maintenance costs. An alternative solution recently proposed to address these problems and called DHW-PV, consists of a group of electric water heaters connected directly to photovoltaic (PV) panels. The first DHW-PV demonstrator was installed in 2019 on social housing. To improve this later, a numerical model of the DHW-PV is required. First, state-of-the-art models have been used to reproduce the performance of individual components. Then, validation has been performed with a model-measurement comparison and a sensitivity analysis. The resulting model shows a good agreement with the demonstrator leading to a mean bias error of 3.5% on the thermal energy transfers and -5.16% on the electricity produced with the PV plant.
Bartholomäus, M., Poulsen, P.B., Dhimish, M., Spataru, S.V. (2025, June 8-13). Evaluating IV curve derived features for fault detection. IEEE PVSC 2025, Montreal, Quebec, Canada.
Abstract
IV curves contain diagnostic information which characterizes faults in photovoltaic systems. Past research used IV curve derived features for fault detection, but a systematic investigation of features in outdoor conditions is missing. In this work, we perform outdoor IV measurements on module level with varying penetration of potential induced degradation, cell cracks, high series resistance and partial shade. We systematically evaluate eighteen IV derived features derived from the literature, investigating their ability to detect the faults. We calculate the feature’s importance for classifying the faults using machine learning classification methods (ExtraTrees, RandomForest, XGBoost, and GradientBoosting). Classification method-specific differences were observed and later eliminated by averaging the feature importance from all methods. Results show Rs, Vmp/Voc and FF were most important to detect high series resistance, M ppf , Vte and Imp/Isc for potential induced degradation, the FF, Imp/Isc and Vte for cell cracks, and M ppf , Vmp/Voc, Vmp and FF to detect shade at an overall classification accuracy of 95%. Greater importance was found for features that require IV and sensor based irradiance measurements compared to maximum power point monitoring. The method applies to any feature–fault combination and offers a strong indication of which features shall be further considered for fault detection strategies.
Gupta, M., Baize-Roche, F., Das, K. (2025, June 24-27). Hybrid Renewable Power Plants for Green Ammonia Production: Optimal Operation and Economic Assessment. WSEC 2025, Nante, France.
Grandin, G., Gupta, M., Murcia Leon, J.P. (2025, June 24-27). Production of eMethanol in hybrid renewable power plants with energy storage. WSEC 2025, Nante, France.
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