Author
Mathieu David
Bio: Mathieu David is an academic researcher from University of La Réunion. The author has contributed to research in topics: Solar irradiance & Probabilistic logic. The author has an hindex of 25, co-authored 65 publications receiving 2277 citations.
Papers published on a yearly basis
Papers
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TL;DR: In this article, the authors present an in-depth review of the current methods used to forecast solar irradiance in order to facilitate selection of the appropriate forecast method according to needs.
Abstract: Integration of solar energy into the electricity network is becoming essential because of its continually increasing growth in usage. An efficient use of the fluctuating energy output of photovoltaic (PV) systems requires reliable forecast information. In fact, this integration can offer a better quality of service if the solar irradiance variation can be predicted with great accuracy. This paper presents an in-depth review of the current methods used to forecast solar irradiance in order to facilitate selection of the appropriate forecast method according to needs. The study starts with a presentation of statistical approaches and techniques based on cloud images. Next numerical weather prediction or NWP models are detailed before discussing hybrid models. Finally, we give indications for future solar irradiance forecasting approaches dedicated to the management of small-scale insular grids.
599 citations
01 Dec 2017
TL;DR: In this article, the authors present detailed information about solar resource data and the resulting data products needed for each stage of a solar energy project, from initial site selection to systems operations.
Abstract: This report presents detailed information about solar resource data and the resulting data products needed for each stage of a solar energy project, from initial site selection to systems operations. It also contains a summary of solar forecasting and its development throughout the last few years. The U.S. Department of Energy's Solar Energy Technologies Office, project developers, engineering procurement construction firms, utility companies, system operators, energy suppliers, financial investors, and others involved in solar energy systems planning and development will find this handbook to be a valuable resource for the collection and interpretation of solar resource data. This handbook is expected to be used as a reference during each project stage.
211 citations
TL;DR: It is found that for hour ahead solar forecasting, the machine learning techniques slightly improve the performances exhibited by the linear AR and the scaled persistence model, however, the improvement appears to be more pronounced in case of unstable sky conditions.
200 citations
TL;DR: This paper assesses the performances of a commonly used combination of two linear models (ARMA and GARCH) in econometrics in order to provide probabilistic forecasts of solar irradiance and the proposed model is able to perform point forecasts as accurately as other methods based on machine learning techniques.
150 citations
Agency for Science, Technology and Research1, National Center for Atmospheric Research2, University of La Rioja3, University of Bucharest4, University of the Faroe Islands5, West University of Timișoara6, University of South Australia7, National University of Singapore8, University of California, San Diego9, University of La Réunion10, Ibn Tofail University11, University of North Carolina at Charlotte12, University of New South Wales13, Fraunhofer Society14, Uppsala University15, University at Albany, SUNY16, Technical University of Madrid17, Massachusetts Institute of Technology18, PSL Research University19, Harbin Institute of Technology20, University of Oregon21, University of Texas at Dallas22
TL;DR: To analyze and compare solar forecasts, the well-established Murphy–Winkler framework for distribution-oriented forecast verification is recommended as a standard practice and the use of the root mean square error (RMSE) skill score based on the optimal convex combination of climatology and persistence methods is highly recommended.
129 citations
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01 Jan 1989
TL;DR: In this article, a two-dimensional version of the Pennsylvania State University mesoscale model has been applied to Winter Monsoon Experiment data in order to simulate the diurnally occurring convection observed over the South China Sea.
Abstract: Abstract A two-dimensional version of the Pennsylvania State University mesoscale model has been applied to Winter Monsoon Experiment data in order to simulate the diurnally occurring convection observed over the South China Sea. The domain includes a representation of part of Borneo as well as the sea so that the model can simulate the initiation of convection. Also included in the model are parameterizations of mesoscale ice phase and moisture processes and longwave and shortwave radiation with a diurnal cycle. This allows use of the model to test the relative importance of various heating mechanisms to the stratiform cloud deck, which typically occupies several hundred kilometers of the domain. Frank and Cohen's cumulus parameterization scheme is employed to represent vital unresolved vertical transports in the convective area. The major conclusions are: Ice phase processes are important in determining the level of maximum large-scale heating and vertical motion because there is a strong anvil componen...
3,813 citations
TL;DR: An overview of forecasting methods of solar irradiation using machine learning approaches is given and it will be shown that other methods begin to be used in this context of prediction.
1,095 citations
TL;DR: This paper appears with the aim of compiling a large part of the knowledge about solar power forecasting, focusing on the latest advancements and future trends, and represents the most up-to-date compilation of solarPower forecasting studies.
829 citations
TL;DR: A critical review of the existing body of knowledge of researches related to green building is presented in this article, where the common research themes and methodology were identified, such as the definition and scope of green building, quantification of benefits of green buildings compared to conventional buildings, and various approaches to achieve green buildings.
Abstract: Green building is one of measures been put forward to mitigate significant impacts of the building stock on the environment, society and economy. However, there is lack of a systematic review of this large number of studies that is critical for the future endeavor. The last decades have witnessed rapid growing number of studies on green building. This paper reports a critical review of the existing body of knowledge of researches related to green building. The common research themes and methodology were identified. These common themes are the definition and scope of green building; quantification of benefits of green buildings compared to conventional buildings; and various approaches to achieve green buildings. It is found that the existing studies played predominately focus on the environmental aspect of green building. Other dimensions of sustainability of green building, especially the social sustainability is largely overlooked. Future research opportunities were identified such as effects of climatic conditions on the effectiveness of green building assessment tools, validation of real performance of green buildings, unique demands of specific population, and future proofing.
808 citations
TL;DR: In this paper, a comprehensive and systematic review of the direct forecasting of PV power generation is presented, where the importance of the correlation of the input-output data and the preprocessing of model input data are discussed.
Abstract: To mitigate the impact of climate change and global warming, the use of renewable energies is increasing day by day significantly. A considerable amount of electricity is generated from renewable energy sources since the last decade. Among the potential renewable energies, photovoltaic (PV) has experienced enormous growth in electricity generation. A large number of PV systems have been installed in on-grid and off-grid systems in the last few years. The number of PV systems will increase rapidly in the future due to the policies of the government and international organizations, and the advantages of PV technology. However, the variability of PV power generation creates different negative impacts on the electric grid system, such as the stability, reliability, and planning of the operation, aside from the economic benefits. Therefore, accurate forecasting of PV power generation is significantly important to stabilize and secure grid operation and promote large-scale PV power integration. A good number of research has been conducted to forecast PV power generation in different perspectives. This paper made a comprehensive and systematic review of the direct forecasting of PV power generation. The importance of the correlation of the input-output data and the preprocessing of model input data are discussed. This review covers the performance analysis of several PV power forecasting models based on different classifications. The critical analysis of recent works, including statistical and machine-learning models based on historical data, is also presented. Moreover, the strengths and weaknesses of the different forecasting models, including hybrid models, and performance matrices in evaluating the forecasting model, are considered in this research. In addition, the potential benefits of model optimization are also discussed.
626 citations