Julien Nou

Intra-Day DNI Forecasting Under Clear Sky Conditions Using ANFIS

Abstract The present paper deals with the developement of a new intra-day Direct Normal Irradiance (DNI) forecasting methodology, under clear sky conditions. Indeed, one challenge of the CSPIMP (Concentrated Solar Power plant efficiency IMProvement) research project is to forecast the suns resource accurately and design efficient plant control approaches. First, a quick review of the different formulations available for the atmospheric turbidity coefficient (from which DNI can be calculated) is performed. The data selection and filtering process is then described. Finally, the new forecasting approaches are compared to persistent and autoregressive models. The most efficient model presented here is based on side-by-side Adaptive Network-based Fuzzy Inference Systems (ANFIS). The selected configuration achieves very good results and validates the proposed forecasting methodology.

Towards the intrahour forecasting of direct normal irradiance using sky-imaging data

Increasing power plant efficiency through improved operation is key in the development of Concentrating Solar Power (CSP) technologies. To this end, one of the most challenging topics remains accurately forecasting the solar resource at a short-term horizon. Indeed, in CSP plants, production is directly impacted by both the availability and variability of the solar resource and, more specifically, by Direct Normal Irradiance (DNI). The present paper deals with a new approach to the intrahour forecasting (the forecast horizon Δtf is up to 30min ahead) of DNI, taking advantage of the fact that this quantity can be split into two terms, i.e. clear-sky DNI and the clear sky index. Clear-sky DNI is forecasted from DNI measurements, using an empirical model (Ineichen and Perez, 2002) combined with a persistence of atmospheric turbidity. Moreover, in the framework of the CSPIMP (Concentrating Solar Power plant efficiency IMProvement) research project, PROMES-CNRS has developed a sky imager able to provide High Dynamic Range (HDR) images. So, regarding the clear-sky index, it is forecasted from sky-imaging data, using an Adaptive Network-based Fuzzy Inference System (ANFIS). A hybrid algorithm that takes inspiration from the classification algorithm proposed by Ghonima et al. (2012) when clear-sky anisotropy is known and from the hybrid thresholding algorithm proposed by Li et al. (2011) in the opposite case has been developed to the detection of clouds. Performance is evaluated via a comparative study in which persistence models – either a persistence of DNI or a persistence of the clear-sky index – are included. Preliminary results highlight that the proposed approach has the potential to outperform these models (both persistence models achieve similar performance) in terms of forecasting accuracy: over the test data used, RMSE (the Root Mean Square Error) is reduced of about 20Wm−2, with Δtf=15min, and 40Wm−2, with Δtf=30min.

Solar Thermal and Geothermal Hybrid Process

This paper is based on a project combining solar and geothermal energies used for heating an individual home located in a mountainous region of southern France (Saint-Pierre Dels Forcats town). A solar field composed of 10 thermal solar collectors, with a total area of 21.25m2 increases the borehole (one hundred meters of depth) temperature. A heat pump extracts the heat energy to restore it to the home with a heating floor of 100m2. The project objective is to develop a control on the global system to realize financial and energy savings (thermal and electric). This paper presents (i) the study of the process (data acquisition and energy analysis) and (ii) the first results of the modeling and the control strategy that will be developed.