Influence of global solar radiation typical days on forecasting models error

Paper presented to the 3rd Southern African Solar Energy Conference, South Africa, 11-13 May, 2015.In this work, we have led an analysis of different global solar radiation forecasting models errors according to the global solar radiation variability. Different predictions models were performed such as machine learning techniques (Neural Networks, Gaussian processes and support vector machines) in order to forecast the Global Horizontal solar Irradiance (GHI). We also include in this study a simple linear autoregressive (AR) model as well as two naïve models based on persistence of the GHI and persistence of the clear sky index (denoted herein scaled persistence model). The models are calibrated and tested with data from three French islands: Corsica (42.15°N ; 9.08°E), Guadeloupe (16.25°N ; 61.58°W) and Reunion (21.15°S ; 55.5°E). Guadeloupe and Reunion are located in a subtropical climatic zone whereas Corsica is in a tempered climatic zone hence, the global solar radiation variation differs significantly. The output error of the different models was quantified by the normalized root mean square error (nRSME). In order to quantify the influence of the global solar radiation variability on the forecasting models error we performed a classification of typical days. Each class of day is defined by a global solar radiation variability rate. For each class and for each location, forecasting models were performed and the error was quantified. With this analysis, global solar radiation forecasting models can be selected according to the location, the global solar radiation fluctuations and hence the meteorological conditions.cf201

Dosimetric uncertainties related to the elasticity of bladder and rectal walls: Adenocarcinoma of the prostate

Purpose. - Radiotherapy is an important treatment for prostate cancer.During treatment sessions, bladder and rectal repletion is difficult to quantify and cannot be measured with a single and initial CT scan acquisition. Some methods, such as image-guided radiation therapy and dose-guided radiation therapy, aimto compensate thismissing information through periodic CT acquisitions. The aimis to adapt patient's position, beam configuration or prescribed dose for a dosimetric compliance. Methods. -We evaluated organmotion (and repletion) for 54 patients after having computed the original ballistic on a new CT scan acquisition. A new delineation was done on the prostate, bladder and rectum to determine the newdisplacements and define organ dosesmistakes (equivalent uniformdose, average dose and dose-volume histograms). Results. - The new CT acquisitions confirmed that bladder and rectal volumes were not constant during sessions. Some cases showed that previously validated treatment plan became unsuitable. A proposed solution is to correct dosimetries when bladder volume modifications are significant. The result is an improvement for the stability of bladder doses, D50 error is reduced by 25.3%, mean dose error by 5.1% and equivalent uniform dose error by 2.6%. For the rectum this method decreases errors by only 1%. This process can reduce the risk of mismatch between the initial scan and following treatment sessions. Conclusion. - For the proposedmethod, the cone-beamCT is necessary to properly position the isocenter and to quantify bladder and rectal volume variation and deposited doses. The dosimetries are performed in the event that bladder (or rectum) volume modification limits are exceeded. To identify these limits, we have calculated that a tolerance of 10% for the equivalent uniformdose (compared to the initial value of the first dosimetry), this represents 11% of obsolete dosimetries for the bladder, and 4% for the rectum

Predicting Global Irradiance Combining Forecasting Models Through Machine Learning

This paper has been presented at : 13th International Conference on Hybrid Artificial Intelligence Systems (HAIS 2018)Predicting solar irradiance is an active research problem, with many physical models having being designed to accurately predict Global Horizontal Irradiance. However, some of the models are better at short time horizons, while others are more accurate for medium and long horizons. The aim of this research is to automatically combine the predictions of four different models (Smart Persistence, Satellite, Cloud Index Advection and Diffusion, and Solar Weather Research and Forecasting) by means of a state-of-the-art machine learning method (Extreme Gradient Boosting). With this purpose, the four models are used as inputs to the machine learning model, so that the output is an improved Global Irradiance forecast. A 2-year dataset of predictions and measures at one radiometric station in Seville has been gathered to validate the method proposed. Three approaches are studied: a general model, a model for each horizon, and models for groups of horizons. Experimental results show that the machine learning combination of predictors is, on average, more accurate than the predictors themselves.The authors are supported by the Spanish Ministry of Economy and Competitiveness, projects ENE2014-56126-C2-1-R and ENE2014-56126-C2-2-R and FEDER funds. Some of the authors are also funded by the Junta de Andalucía (research group TEP-220)

Non-linear Autoregressive Neural Networks to Forecast Short-Term Solar Radiation for Photovoltaic Energy Predictions

Nowadays, green energy is considered as a viable solution to hinder CO2 emissions and greenhouse effects. Indeed, it is expected that Renewable Energy Sources (RES) will cover 40% of the total energy request by 2040. This will move forward decentralized and cooperative power distribution systems also called smart grids. Among RES, solar energy will play a crucial role. However, reliable models and tools are needed to forecast and estimate with a good accuracy the renewable energy production in short-term time periods. These tools will unlock new services for smart grid management. In this paper, we propose an innovative methodology for implementing two different non-linear autoregressive neural networks to forecast Global Horizontal Solar Irradiance (GHI) in short-term time periods (i.e. from future 15 to 120min). Both neural networks have been implemented, trained and validated exploiting a dataset consisting of four years of solar radiation values collected by a real weather station. We also present the experimental results discussing and comparing the accuracy of both neural networks. Then, the resulting GHI forecast is given as input to a Photovoltaic simulator to predict energy production in short-term time periods. Finally, we present the results of this Photovoltaic energy estimation discussing also their accuracy

A newly developed integrative bio-inspired artificial intelligence model for wind speed prediction

Accurate wind speed (WS) modelling is crucial for optimal utilization of wind energy. NumericalWeather Prediction (NWP) techniques, generally used for WS modelling are not only less cost-effective but also poor in predicting in shorter time horizon. Novel WS prediction models based on the multivariate empirical mode decomposition (MEMD), random forest (RF) and Kernel Ridge Regression (KRR) were constructed in this paper better accuracy in WS prediction. Particle swarm optimization algorithm (PSO) was employed to optimize the parameters of the hybridized MEMD model with RF (MEMD-PSO-RF) and KRR (MEMD-PSO-KRR) models. Obtained results were compared to those of the standalone RF and KRR models. The proposed methodology is applied for monthly WS prediction at meteorological stations of Iraq, Baghdad (Station1) and Mosul (Station2) for the period 1977-2013. Results showed higher accuracy of MEMD-PSO-RF model in predicting WS at both stations with a correlation coefficient (r) of 0.972 and r D 0.971 during testing phase at Station1 and Station2, respectively. The MEMD-PSO-KRR was found as the second most accurate model followed by Standalone RF and KRR, but all showed a competitive performance to the MEMD-PSO-RF model. The outcomes of this work indicated that the MEMD-PSO-RF model has a remarkable performance in predicting WS and can be considered for practical applications

Periodic autoregressive forecasting of global solar irradiation without knowledge-based model implementation

Reliable forecasting methods increase the integration level of stochastic production and reduce cost of intermittence of photovoltaic production. This paper proposes a solar forecasting model for short time horizons, i.e. one to six hours ahead. In this time-range, machine learning methods have proven their efficiency. But their application requires that the solar irradiation time series is stationary which can be realized by calculating the clear sky global horizontal solar irradiance index (CSI), depending on certain meteorological parameters. This step is delicate and often generates additional uncertainty if conditions underlying the calculation of the CSI are not well-defined and/or unknown. As a novel alternative, we introduce a so-called periodic autoregressive (PAR) model. We discuss the computation of post-sample point forecasts and forecast intervals. We show the forecasting accuracy of the model via a real data set, i.e., the global horizontal solar irradiation (GHI) measured at two meteorological stations located at Corsica Island, France. In particular, and as opposed to methods based on CSI, a PAR model helps to improve forecast accuracy, especially for short forecast horizons. In all the cases, PAR is more appropriate than persistence, and smart persistence. Moreover, smart persistence based on the typical meteorological year gives more reliable results than when based on CSI