Optimisation de l'intégration de données multi-sources dans les modèles de prévision court-terme de la production photovoltaïque

Dans un contexte d’épuisement des ressources naturelles, les sources d’énergies renouvelables jouent un rôle croissant dans le mix de la production électrique. Cependant, une part importante des renouvelables peut compromettre la stabilité du réseau électrique en raison de leurs variabilités. Il est donc primordial de connaître la quantité d’énergie future produite afin d’assurer l’équilibre entre production et consommation. Cette thèse porte sur l’amélioration de la précision des prévisions court-terme de la production photovoltaïque. Pour y parvenir, un couplage entre modèles statistiques et modèles physiques est proposé, en plus d’une architecture permettant de conditionner les modèles à la situation météorologique. En outre, un large éventail de sources d’information est considéré. A cet égard, une analyse approfondie des données permet de mettre en exergue l’information pertinente ainsi que les dépendances spatio-temporelles pouvant exister entre les différentes variables.In a context of natural resources depletion, weather-dependent renewable energy sources play an increasingly important role in the electricity generation mix. Yet, high shares of renewables can jeopardise the safe operation of the power grid due to their variable nature. To address this challenge, it is essential to know the future amount of energy produced to balance production and consumption. In this thesis, we explore two main approaches that aim at improving the accuracy of short-term photovoltaic generation forecasting. The first option is to extend the existing statistical models found in the literature through the coupling with a physics-based model, and by operating a shift from static to weather-adaptive models. The second option lies in extending the range of available sources of information. In this regard, an in-depth quality analysis of production measurements emphasises relevant information, and exhibits the spatio-temporal correlations that may exist between the inputs

Short-Term Photovoltaic Generation Forecasting Enhanced by Satellite Derived Irradiance

International audienceIn a context of natural resources depletion, weather-dependent renewable energy sources play an increasingly important role in the energy mix. Yet, high shares of renewables can jeopardise the safe operation of power grid due to their variable nature. To address this challenge, it is essential to know the future amount of energy produced to balance production and consumption. This paper aims at investigating photovoltaic generation short-term forecasting and particularly spatio-temporal approaches. These approaches permit to exploit the spatial dependency of weather variables to provide valuable information regarding cloud movements. Thus, it is possible for a power producer to take advantage of dense PV plants networks by considering spatially distributed units as remote sensors. For low-density network, satellite-derived information observed in the vicinity of the power unit location offers an interesting alternative. To reduce the computational burden induced by this data source, feature-selection approaches are implemented. Usually, a correlation score is used to measure the dependence between lagged satellite-based time-series with the target feature (i.e. power production observations). However, this approach tends to provide redundant information (i.e. highly correlated pixels). To address this issue, we implement a minimal-Redundance Maximal-Relevance framework. Performance comparisons with state-of-the-art approaches are also performed

Short-term photovoltaic generation forecasting using multiple heterogenous sources of data based on an analog approach.

International audienceOver the past years, environmental concerns have played a key role in the development of renewable energy sources (RES). In Europe, the installed capacity of photovoltaic (PV) has increased from around 10 GW in 2008 to nearly 119 GW in 2018 [1]. Due to this high penetration rate and the intermittent nature of RES, several challenges appear related to the economic and secure operation of a power system. To overcome these challenges, it is necessary to develop reliable forecasts of RES, and namely of PV production, for the next hours to days to adjust production planning, while intra-hourly forecasts may contribute to optimize operation of storage units coupled to RES plants.The aim of this paper is to present a novel spatio-temporal (ST) spot forecasting approach able to use multiple heterogeneous sources of data as inputs to forecast short-term PV production (i.e. from 15 minutes up to a day ahead).First, we consider measured production data from nearby power plants as input to forecast the output of a specific PV plant. These data permit to exploit the correlation between the production data of spatially distributed PV sites. The classical ST approach in the literature, based only on this source of data [2], permits to improve predictability for the next few minutes up to 6 hours ahead.Then, we extend the model by the use of satellite images (i.e. global horizontal irradiance (GHI)) which provide meaningful spatial information at a larger extent.Finally, we consider Numerical Weather Predictions (NWPs) as input, which permit to extend the applicability of the model to day-ahead lead times, so that, overall, the resulting model covers efficiently horizons ranging from a few minutes to day ahead.The spatio-temporal relationships being dependent on the particular meteorological situation of the day at hand, we apply an analog ensemble approach, to condition the learning process with historical observations corresponding to similar meteorological situation. We used the analogue approach to select a subset of similar historical situations over which a dynamical calibration of the forecasted model is done, as it was for example suggested by [3,4]. In our paper we extend the analogs ensemble approach by considering geographically distributed observations of the physical variables of interest (as suggested by [4] for hydrological issues) rather than only those at the level of the PV plant.The performance of the proposed ST model with heterogeneous inputs is compared with reference models and advanced ones such as the Random Forest model. Historical production data collected from 9 PV plants of CNR are considered. The power units, located in the South-East France, exhibit relevant spatial correlations which make them suitable for the proposed ST model.References:[1] IRENA - https://www.irena.org/Statistics/Download-Data[2] Agoua, Girard, Kariniotakis. Short-Term Spatio-Temporal Forecasting of Photovoltaic Power Production. IEEE Transactions on Sustainable Energy, IEEE, 2018, 9 (2), pp. 538 - 546. https://doi.org/10.1109/TSTE.2017.2747765[3] Alessandrini, Delle Monache, Sperati, Cervone. An analog ensemble for short-term probabilistic solar power forecast. Applied Energy, 2015. https://doi.org/10.1016/j.apenergy.2015.08.0114] Bellier, Bontron, Zin. Using meteorological analogues for reordering postprocessed precipitation ensembles in hydrological forecasting. Water Resources Research, 2017. https://doi.org/10.1002/201

Integration of Physical Knowledge in Statistical Photovoltaic Production Forecasting Models

Photovoltaic (PV) production is characterised by a high variability originating from complex processes involving the combination of astronomical, meteorological, environmental, and technological factors. Irradiance reaching the PV panels is modulated through the Sun’s position in the sky dome, and depends on the composition of the atmosphere, the presence of surrounding obstacles, and also on the inclination angle and optical properties of the module’s cover. Then, during the conversion process into electric power, ambient conditions such as temperature impact negatively cell’s efficiency. This variability raises concerns in a context of high penetration of renewables, and requires efficient forecasting tools to guarantee a safe and profitable operation of the grid. In the literature, we observe a dichotomy between the nature of forecasting tools traditionally used: one can explicitly model the conversion laws at stake, or consider data-driven models that implicitly derive these relations. In an attempt to merge these two fields, we propose a simple physics-based model that converts irradiance into power-like feature as a pre-processing step before the integration into a Machine Learning (ML) model. This contributes to reduce the inferring effort by explicitly integrating plants technical properties within regression models. In contrast to ML models, it is common practice in the time-series forecasting domain to resort to clear-sky normalisation methods to provide stationary inputs. Typically, this process only clears the PV production signal from its dependency on the Sun’s path. We propose to go further by applying the conversion model in a clear-sky normalisation framework to remove dependencies on plant geometry and ambient environmental conditions. This approach turns out to slightly improve local stationary properties of production time series, and to impact positively the forecasting performances of the studied ML model in comparison with the consideration of raw features. In addition, we highlight that the ML model is able to derive, to some extent, the conversion laws when fed with a set of relevant features intervening in the conversion chain

Short-term Photovoltaic Power Forecasting Enhanced by Heterogeneous Sources of Spatio-temporal Information

The power output of Photovoltaic (PV) plants is weather-dependent, which leads to inherent uncertainties regarding the future production.With the growing share of renewables in the energy mix, such a characteristic raises technical challenges regarding the safe operation of the grid, and impacts the profitability of producers involved in electricity markets. This motivates the development of accurate forecasting tools for horizons ranging from a few minutes up to several days ahead. Two levers are usually available to improve the accuracy of predictions: forecasting tools, and input data. With the development of PV plants, and the advances in smart monitoring and measurements, we observe a paradigm shift from temporal- to spatio-temporal-based forecasting models. This family of models considers features that exploit spatio-temporal correlations in the data, such as observations from spatially distributed portfolios of PV plants or satellite-derived information. In this paper we provide a full assessment of the value of spatio-temporal data. First, the limits of a PV portfolio are highlighted through an analysis of the local topography and wind distribution at several altitudes. This motivates the use of 2D satellite-based maps. A features selection approach that we originally applied to the PV forecasting field is implemented to deal with the induced dimensionality burden. This approach enables the derivation of low-redundant features fairly distributed around the power plant. Lastly, we consider cloud opacity maps obtained from infrared channels. Despite being under-represented in the literature (only two studies have been found), infrared channel-based data present the advantage of offering nighttime observations of cloud cover, which contributes to improving early morning forecasts. This paper demonstrates the scientific interest of opacity maps compared with satellite-derived irradiance and irradiance forecasts for the field of short-term PV power forecasting. Evaluations are performed on real-world datasets composed of nine PV plants

A generic methodology to efficiently integrate weather information in short-term Photovoltaic generation forecasting models

International audienceThe power output of Photovoltaic (PV) plants is weather-dependent, which results in inherent uncertainties about future production. This raises technical challenges for grid operators, especially in power systems with high PV penetration, and also financial losses when PV generation is traded on electricity markets. Accurate forecasts for the next hours or days contribute to alleviating these impacts. The literature features a plethora of forecasting models, among which outstanding approaches combine heterogeneous sources of inputs like measurements, weather forecasts and satellite images. The integration of such inputs into forecast models can take two forms: either as explanatory features, or as state features that condition the model training through a local regression approach. With the latter, physics-based information can be included within statistical regression tools to derive optimised models w.r.t. weather input. These models are then extended to integrate spatio-temporal information from satellite observations. We investigate these approaches with the objective of deriving the mathematical foundations of a generic methodology to integrate weather information into PV forecasting models. The paper assesses the influence of weather information integration strategies on forecasting performances for two state-of-the-art short-term forecasting models, belonging respectively to linear and non-linear families. Lastly, general guidelines for forecasters are derived regarding the procedure to follow when dealing with several sources of information. Evaluations are performed on real-world datasets composed of nine PV plants

Forecasting regional wind production based on weather similarity and site clustering for the EEM20 Competition

International audiencePrecise probabilistic forecasting tools of wind power generation are essential for solving problems related to the impact of wind generation uncertainty on electrical systems. In this context, the International Conference on the European Energy Market EEM20 set up a competition aiming at forecasting wind power generation for the four Swedish bidding zones. Participants had to generate day-ahead quantile forecasts of the aggregated wind power production from Numerical Weather Predictions (NWPs) ensembles provided all over the country [1]

Use of Several Sources of Spatio-temporal Information to Improve Short-term Photovoltaic Power Forecasting

International audienceIn recent years, the share of photovoltaic (PV) power in Europe has grown: the installed capacity increased from around 10 GW in 2008 to nearly 185 GW in 2021. Due to the intermittent nature of PV generation, new challenges arise regarding economic profitability and the safe operation of the power network. To overcome these issues, a special effort is made to develop efficient PV generation forecasting tools.Several sources of information are currently investigated in the literature. Each one possesses different characteristics, which make them horizon-specific. For short-term forecasting (i.e. from a few minutes to 6-hour ahead), endogenous inputs, namely past PV production measurements, are typically the main drivers. With the development of PV plants, and the advances in smart monitoring and measurements, we observe a paradigm shift from temporal- to spatio-temporal (ST)-based forecasting models. This family of models considers features that exploit ST correlations in the data, such as observations from spatially distributed portfolios of PV plants. This new paradigm offers power producers the possibility to economically value information from geographically distributed plant networks in the form of forecast accuracy improvements, and prepares the ground for a data-sharing market.Depending on its distribution or density, a PV network may partially account for the complex ST processes at stake (e.g. mainly sites located upwind or crosswind). To fill this gap, satellite-based observations are an appealing option. With recent developments, geostationary satellites can capture images of Earth at a temporal resolution of less than an hour, which enables operational uses. Contrary to the spatial inflexibility inherent to PV networks, satellite-based observations offer the possibility of covering the whole vicinity of the site location, and much more. In that context, relevant features selection tools need to be considered.In this work, we propose the following contributions to the state of the art. Traditionally in the literature, observations from spatially distributed units and satellite-derived information are used separately. We propose an incremental approach to assess the impact of one or several sources of data on the forecasting performances of the considered regression model. This approach shows that the combination of various sources of ST information leads to higher accuracy than when inputs are considered individually. This is assumed to result from the difference in spatial resolutions of both features. In this specific case study, we highlight the limits of the PV plants portfolio through an analysis of the local topography and wind distribution at several altitudes Then, we consider cloud opacity maps obtained from infrared channels. Despite being under-represented in the literature (only two studies have been found), infrared channel-based data present the advantage of offering nighttime observations of cloud cover, which contributes to improving early morning forecasts.The proposed approaches are evaluated using 9 PV plants in France and for a testing period of 12 months

Short-term Forecasting of Photovoltaic Generation based on Conditioned Learning of Geopotential Fields

International audienceDue to environmental concerns, renewable energy sources (RES) play an increasingly important role in the energy mix. In France, from 2018 to 2019, an increase of 21.2% and 7.8% of energy production was observed for wind and solar respectively [1]. RES are characterized by high variability and limited predictability, mostly due to their dependence on meteorological factors. This variability presents challenges for RES integration into grids and electricity markets: as the penetration of RES increases, power system balancing becomes more complex, and congestions may occur in the grid. This lack of predictability can also have financial consequences. In some European countries, energy producers have to pay penalties proportional to the forecasting error of the injected power. To address these challenges, it is important to accurately predict the future amount of energy production. In this paper we propose a spot statistical forecasting model for very short-term time horizons (from a few minutes up to 6 hours ahead). This model is based on a combination of heterogeneous inputs with a conditioned learning approach. Spatio-temporal inputs (measurements from geographically distributed PV sites and satellite images) are used to enhance short-term predictability, while a weather analog approach enables adaptability to changes in meteorological conditions by considering the most relevant past observations. The performance evaluations are carried out on a case study featuring nine PV plants located in France, over a one-year period

Probabilistic Forecasting of Regional Wind Power Generation for the EEM20 Competition: a Physics-oriented Machine Learning Approach

International audienceVariable renewable energy has a growing impact on electricity markets and power systems in many regions of the world. In this context, the 17th International Conference on the European Energy Market EEM20 set up a competition to develop probabilistic forecasting tools of wind production at a regional level. This paper proposes an adaptive approach for regional wind power forecasting. A physics-oriented pre-processing of the data delivers analog weather patterns and wind-power-related variables, then a k-means clustering of wind farms further reduces the dimension of the problem. The generated representative features feed a Quantile Regression Forests model that produces sharp and reliable predictions. As a result, our model won the competition with a relative improvement of the average pinball loss of 6.7% and 14.7%, compared to the teams ranked second and third respectively