Solar irradiation nowcasting by stochastic persistence: a new parsimonious, simple and efficient forecasting tool

International audienceSimple, naïve, smart or clearness persistences are tools largely used as naïve predictors for the global solar irradiation forecasting. It is essential to compare the performances of sophisticated prediction approaches with that of a reference approach generally a naïve methods. In this paper, a new kind of naïve " nowcaster " is developed, a persistence model based on the stochastic aspect of measured solar energy signal denoted stochastic persistence and constructed without needing a large collection of historical data. Two versions are proposed: one based on an additive and one on a multiplicative scheme; a theoretical description and an experimental validation based on measurements realized in Ajaccio (France) and Tilos (Greece) are exposed. The results show that this approach is efficient, easy to implement and does not need historical data as the machine learning methods usually employed. This new solar irradiation predictor could become an interesting tool and become a new member of the solar forecasting family

Meteorological time series forecasting based on MLP modelling using heterogeneous transfer functions

In this paper, we propose to study four meteorological and seasonal time series coupled with a multi-layer perceptron (MLP) modeling. We chose to combine two transfer functions for the nodes of the hidden layer, and to use a temporal indicator (time index as input) in order to take into account the seasonal aspect of the studied time series. The results of the prediction concern two years of measurements and the learning step, eight independent years. We show that this methodology can improve the accuracy of meteorological data estimation compared to a classical MLP modelling with a homogenous transfer function

Estimation of Tilted Solar Irradiation Using Artificial Neural Networks

AbstractCalculation of solar global irradiation on tilted planes from only horizontal global one is particularly difficult when the time step is small. We used an Artificial Neural Network (ANN) to realize this conversion at an hourly and 10- min time step. The ANN is developed and optimized using five years of solar data. The accuracy is respectively for hourly and 10-min data of 6% and 9% for the RMSE and 3.5% and 5.5% for the RMAE i.e. similar or slightly lower than the errors obtained with conventional empirical correlations for hourly data

Urban ozone concentration forecasting with artificial neural network in Corsica

Atmospheric pollutants concentration forecasting is an important issue in air quality monitoring. Qualitair Corse, the organization responsible for monitoring air quality in Corsica (France) region, needs to develop a short-term prediction model to lead its mission of information towards the public. Various deterministic models exist for meso-scale or local forecasting, but need powerful large variable sets, a good knowledge of atmospheric processes, and can be inaccurate because of local climatical or geographical particularities, as observed in Corsica, a mountainous island located in a Mediterranean Sea. As a result, we focus in this study on statistical models, and particularly Artificial Neural Networks (ANN) that have shown good results in the prediction of ozone concentration at horizon h+1 with data measured locally. The purpose of this study is to build a predictor to realize predictions of ozone and PM10 at horizon d+1 in Corsica in order to be able to anticipate pollution peak formation and to take appropriated prevention measures. Specific meteorological conditions are known to lead to particular pollution event in Corsica (e.g. Saharan dust event). Therefore, several ANN models will be used, for meteorological conditions clustering and for operational forecasting.Comment: Sustainable Solutions for Energy and Environment. EENVIRO 2013, Buchatrest : Romania (2013

Meteorological time series forecasting with pruned multi-layer perceptron and 2-stage Levenberg-Marquardt method

A Multi-Layer Perceptron (MLP) defines a family of artificial neural networks often used in TS modeling and forecasting. Because of its "black box" aspect, many researchers refuse to use it. Moreover, the optimization (often based on the exhaustive approach where "all" configurations are tested) and learning phases of this artificial intelligence tool (often based on the Levenberg-Marquardt algorithm; LMA) are weaknesses of this approach (exhaustively and local minima). These two tasks must be repeated depending on the knowledge of each new problem studied, making the process, long, laborious and not systematically robust. In this paper a pruning process is proposed. This method allows, during the training phase, to carry out an inputs selecting method activating (or not) inter-nodes connections in order to verify if forecasting is improved. We propose to use iteratively the popular damped least-squares method to activate inputs and neurons. A first pass is applied to 10% of the learning sample to determine weights significantly different from 0 and delete other. Then a classical batch process based on LMA is used with the new MLP. The validation is done using 25 measured meteorological TS and cross-comparing the prediction results of the classical LMA and the 2-stage LMA.Comment: International Journal of Modelling, Identification and Control (2014). arXiv admin note: substantial text overlap with arXiv:1308.194

Storage systems for building-integrated photovoltaic (BIPV) and building-integrated photovoltaic/thermal (BIPVT) installations: Environmental profile and other aspects

In recent years there has been an increasing interest in Building-Integrated Photovoltaic (BIPV) and Building-Integrated Photovoltaic/Thermal (BIPVT) systems since they produce clean energy and replace conventional building envelope materials. By taking into account that storage is a key factor in the effective use of renewable energy, the present article is an overview about storage systems which are appropriate for BIPV and BIPVT applications. The literature review shows that there are multiple storage solutions, based on different kinds of materials (batteries, Phase Change Material (PCM) components, etc.). In terms of BIPV and BIPVT with batteries or PCMs or water tanks as storage systems, most of the installations are non-concentrating, façade- or roof-integrated, water- or air-based (in the case of BIPVT) and include silicon-based PV cells, lead-acid or lithium-ion batteries, paraffin- or salt-based PCMs. Regarding parameters that affect the environmental profile of storage systems, in the case of batteries critical factors such as material manufacturing, accidental release of electrolytes, inhalation toxicity, flammable elements, degradation and end-of-life management play a pivotal role. Regarding PCMs, there are some materials that are corrosive and present fire-safety issues as well as high toxicity in terms of human health and ecosystems. Concerning water storage tanks, based on certain studies about tanks with volumes of 300 L and 600 L, their impacts range from 5.9 to 11.7 GJprim and from 0.3 to 1.0 t CO2.eq. Finally, it should be noted that additional storage options such as Trombe walls, pebble beds and nanotechnologies are critically discussed. The contribution of the present article to the existing literature is associated with the fact that it presents a critical review about storage devices in the case of BIPV and BIPVT applications, by placing emphasis on the environmental profile of certain storage materials.The authors would like to thank “Ministerio de Economía y Competitividad” of Spain for the funding (grant reference ENE2016-81040-R). Furthermore, Professor Daniel Chemisana thanks “Institució Catalana de Recerca i Estudis Avançats (ICREA)” for the ICREA Acadèmia award (ICREA Acadèmia 2018)

The case for islands’ energy vulnerability: Electricity supply diversity in 44 global islands

Energy supply security is a multifaceted challenge for all countries and especially for small island nations that might have limited adaptive capacity. Previous studies showed that islands experience energy scarcity and isolation from energy markets due to their remote location making energy supply security a challenging issue. We estimate energy supply diversity and concentration for 44 islands in order to provide an island specific benchmark approach for energy supply security. We use established metrics Shannon-Wiener index (SWI), Herfindahl-Hirschman index (HHI) with Energy Information Administration (EIA) fuel mix data. To confront the issues of supply security and sustainability we test energy diversity against energy and emissions intensity. The global character of the research along with the wide range of islands covered allows useful comparisons between countries and for a means of benchmarking against the indices while creating certain defined country clusters. Overall it is found that average island energy intensity increased by 23.4% with a corresponding increase of 12.4% on their emissions intensity for the period 2000–2015. On the other hand, diversity has improved by 21.3% (SWI) and by 2% (HHI) since 2000. We argue that fossil-fuel lock-in for islands must break in order to UN Sustainable Development Goal 7 to be achieved particularly for vulnerable island nations

Building-integrated solar thermal system with/without phase change material: Life cycle assessment based on ReCiPe, USEtox and Ecological footprint

The present study assesses the environmental profile of a building-integrated solar thermal system that has been developed and tested in France. The investigation is based on life-cycle assessment according to ReCiPe, USEtox and Ecological footprint. Two configurations (for the solar collector) have been examined: 1) Without phase change material (using only rock wool as insulation) and 2) With phase change material (myristic acid) and rock wool. The main goal is the evaluation of the effect of the phase change material on the environmental profile of the solar thermal system. Both cases (with/without phase change material) have been studied based on the Mediterranean climatic conditions of Ajaccio (France). The results, according to ReCiPe midpoint (with characterization) demonstrate that the tubes (copper), the aluminium components (absorber, casing, gutter) and the phase change material are responsible for the highest impacts in terms of the material manufacturing phase of the collectors. With respect to ReCiPe/endpoint/single-score life-cycle results (scenarios: with/without PCM; with/without recycling; including the gutter), the values vary from 0.014 to 0.020 Pts/kWh. The configuration with phase change material presents 0.003 Pts/kWh higher impact (in comparison to the option without phase change material). Recycling offers an impact reduction of 0.003 Pts/kWh (for both configurations with/without phase change material). In addition, results according to USEtox (in terms of human toxicity and ecotoxicity) and Ecological footprint (with respect to the impact categories of carbon dioxide, nuclear and land occupation) are presented and discussed.The authors would like to acknowledge networking support by the COST Action TU1205 Building Integration of Solar Thermal Systems. The authors would also like to thank “Ministerio de Economía y Competitividad” of Spain for the funding (grant reference ENE2016-81040-R)

Numerical weather prediction or stochastic modeling: an objective criterion of choice for the global radiation forecasting

Numerous methods exist and were developed for global radiation forecasting. The two most popular types are the numerical weather predictions (NWP) and the predictions using stochastic approaches. We propose to compute a parameter noted constructed in part from the mutual information which is a quantity that measures the mutual dependence of two variables. Both of these are calculated with the objective to establish the more relevant method between NWP and stochastic models concerning the current problem.Comment: International Journal of Energy Technology and Policy (2014

Kalman filtering and classical time series tools for global radiation prediction

For nowcasting and short term forecasting of salar irradiation, the usual technics are based on machine learning predictions such as Artificial Neural Network (ANN) [1], Support Vector Machines (SVM) [2], AutoRegressive–Moving-Average (ARMA) models [3], etc. A significant inconvenience of these methods is related to the large historic data set required during the training phase of the predictors; thus, in this work, we propose a simple methodology able to predict a global radiation time series without the need of historical data, making the method easily applicable for poor instrumented areas. We suggest to call these intuitive methods in the following “training-less” methods. The accuracy of these methods will be compared against other classical prediction methods, taking into account the time horizon of the prediction