Photovoltaic Forecasting: A state of the art

International audiencePhotovoltaic (PV) energy, together with other renewable energy sources, has been undergoing a rapid development in recent years. Integration of intermittent energy sources as PV or wind power is challenging in terms of power system management in large scale systems as well as in small grids. Indeed, PV energy is a variable resource that is difficult to predict due to meteorological uncertainty. To facilitate the penetration of PV energy, forecasting methods and techniques have been used. Being able to predict the future behavior of a PV plant is very important in order to schedule and manage the alternative supplies and the reserves. In this paper we presented an overview aiming at a classification attending to the different techniques of forecasting methods used for PV or solar prediction. Finally, recent new approaches that take into account the uncertainty of the estimation are introduced. First results of these kind of models are presented

A fuzzy multifactor asset pricing model

International audienceThis paper introduces a new approach of multifactor asset pricing model estimation. This approach assumes that the monthly returns of financial assets are fuzzy random variables and estimates the multifactor asset pricing model as a fuzzy linear model. The fuzzy random representations allows us to incorporate bias on prices induced by the market microstructure noise and to reflect the intra-period activity in the analysis. The application of fuzzy linear regression enables the uncertainty assessment in an alternative way to confidence interval or hypothesis testing, which is subjected the binding assumption of normal distribution of returns. However, it is well known that the distribution of many asset returns deviates significantly from the normal assumption. We illustrate this estimation in the particular case of the Fama and French's (J Financ Econ 33:3-56, 1993) three factor model. Finally, empirical studies based on Fama and French's portfolios and risk factors, historical dataset highlight the effectiveness of our estimation method and a comparative analysis with the ordinary least square estimation shows its ability to be applied for an optimal decision decision making in the financial market

Are rail punctuality indicators useful ? Some evidence from users perceptions

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