Stéphane Grieu

Missing data estimation for energy resources management in tertiary buildings

The BATNRJ project, managed by Pyrescom, focuses on improving energy efficiency while preserving comfort in tertiary buildings. To this end, an open and cost-friendly monitoring solution based on instrumentation as well as analysis and control tools is being developed. Solar radiation and indoor temperature being key parameters, the present paper deals with estimating missing data in case of sensor failures. First, solar radiation is interpolated using as a basis the Gaussian or the Cosine function. Mean relative error is about 10%. Then, based on the concept of time series, feedforward artificial neural networks are used to estimate up to the next 24 hours missing data about indoor temperature. We obtained accurate results, especially for failures limited to 3 hours. The mean relative error does not exceed 6%, even in case of long sensor failures.

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.

Fuzzy rule-based model for optimum orientation of solar panels using satellite image processing

In solar energy converting systems, a particular attention is paid to the orientation of solar collectors in order to optimize the overall system efficiency. In this context, the collectors can be fixed or oriented by a continuous solar tracking system. The proposed approach is based on METEOSAT images processing in order to detect the cloud coverage and its duration. These two parameters are treated by a fuzzy inference system deciding the optimal position of the solar panel. In fact, three weather cases can be considered: clear, partly covered or overcast sky. In the first case, the direct sunlight is more important than the diffuse radiation, thus the panel is always pointed towards the sun. In the overcast case, the solar beam is close to zero and the panel is placed horizontally to receive the diffuse radiation. Under partly covered conditions, the fuzzy inference system decides which of the previous positions is more efficient. The proposed approach is implemented using experimental prototype located in Perpignan (France). On a period of 17 months, the results are very satisfactory, with power gains of up to 23 % compared to the collectors oriented by a continuous solar tracking.

High temperature measurements in irradiated environment using Raman fiber optics distributed temperature sensing

Optical fiber temperature sensors using Raman effect are a promising technology for temperature mapping of nuclear power plant pipes. These pipes are exposed to high temperature (350 °C) and gamma radiations, which is a harsh environment for standard telecom fibers. Therefore metal coated fibers are to be used to perform measurement over 300 °C. Temperature variations can affect the attenuation of the metallic coated fiber before irradiation. The latter induces an extra attenuation, due to light absorption along the fiber by radiation-induced defects. The recombination of these defects can be strongly accelerated by the high temperature value. As backscattered Raman signal is weak it is important to test optical fibers under irradiation to observe how it gets attenuated. Different experiments are described in this conference paper: two in situ irradiation campaigns with different dose rates at, both ambient and high temperature. We observe that the tested off-the-shelf metallic coated fibers have a high attenuation under irradiation. We also noticed the fact that thermal annealing plays a massive role in the +300 °C temperature range.

Predictive Control of Multizone HVAC Systems in Non-residential Buildings

Abstract In France, buildings account for a large part of the energy consumption and carbon emissions. Both are mainly due to Heating, Ventilation and Air-Conditioning (HVAC) systems. So, the present work deals with the predictive control of multizone HVAC systems in non-residential buildings. We used the PMV (Predicted Mean Vote) index as a thermal comfort indicator and developed low-order ANN-based models to be used as controllers internal models. A genetic algorithm allowed the optimization problem to be solved. The proposed strategy allows the operation time of each HVAC sub-system to be optimized (and, as a result, electrical power consumption) and thermal comfort requirements to be met. In order to test this approach, a real non-residential building located in Perpignan (south of France) has been modelled using the EnergyPlus software. The results we obtained in simulation allows the pertinence of the predicitive strategy to be highlighted.

Predictive Control and Optimal Design of Thermal Storage Systems for Multi-energy District Boilers

Abstract As part of the second phase of the OptiEnR research project, the present work deals with improving the operation of a multi-energy district boiler by adding to the plant an optimally designed and controlled thermal storage tank. Previous study focused on both a design approach, based on a parametric analysis, and a non-predictive control strategy. The aim of the present work was to develop a Model Predictive Controller (MPC) to improve the management of the tank in real time. The proposed controller generates optimal command sequences dealing with the amount of thermal energy to be stored or released. As a result, both the fossil energy consumption and CO 2 emissions are significantly reduced while the economic gain is increased.