Wahiba Yaïci

Prediction of the performance of a solar thermal energy system using adaptive neuro-fuzzy inference system

This study investigates the applicability of adaptive neuro-fuzzy inference system (ANFIS) approach for predicting the performance parameters of a solar thermal energy system (STES). Experiments were conducted on the STES during the summer season and for different Canadian weather conditions in Ottawa. The experimental data were used for training and testing the ANFIS network model. The model was then optimised. The predicted values were found to be in very good agreement with the experimental values with mean relative error less than 0.18% and 3.26% for the preheat tank stratification temperatures and the solar fractions, respectively. The results demonstrate that the ANFIS approach can provide high accuracy and reliability for predicting the performance of thermal energy systems.

Artificial neural network modelling for performance prediction of solar energy system

This study sought to investigate the effect of the number of input variables on both the accuracy and the robustness of the artificial neural network (ANN) method for predicting the performance parameters of a solar energy system. Tests were conducted on a solar energy system in Ottawa, Canada during summer under different weather conditions. Three different ANN models, i.e., one each with nine, eight and seven input variables, were developed and compared to a baseline ANN model previously developed by the authors [14]. The experimental data were used for constructing the ANN models in order to assess their reliability. Each of the models was applied in an effort to predict several performance parameters of the system. The data revealed that the optimal algorithms and topologies were the Levenberg-Marquardt algorithm and the structure with 9/8/7 inputs, 20 hidden and 8 outputs, respectively. The simulation results demonstrated the efficiency of this approach and delivered a good measure of precision, even with models employing reduced input variables. However, it is likely true that the degree of model accuracy would gradually decrease with reduced inputs. Overall, the results of this contribution reveal that the ANN technique provides both high precision and strong robustness for predicting the performance of highly nonlinear energy systems.

Experimental and Simulation Study on a Solar Domestic Hot Water System With Flat-Plate Collectors for the Canadian Climatic Conditions

This paper presents thermal performance results of an experimental and numerical simulation study of a solar domestic hot water system (SDHW) for Canadian weather conditions. The experimental test setup includes two solar panels, a solar preheat tank, and an auxiliary propane-fired storage water heater, and an air handler unit for space heating. Experiments were performed on the SDHW system during a different season of the year, over the period March through October 2011 to assess the system performance for different solar gain and water draw schedules. Sunny, partly cloudy and cloudy conditions were explored. The test results were analysed in terms of solar fraction, solar efficiency, and the effects of thermosyphoning and stratification in the solar storage tank.Modelling and simulation of the solar thermal energy system using TRNSYS software was performed. The objective was to optimise key design parameters and to suggest an effective control strategy to maximise the heat extraction from solar collectors. The developed model was based on the experimental test setup. It was first adjusted and verified with the solar gain and water draw schedule experimental data. The results of the numerical simulations were then validated with experimental results obtained with other water draw schedule and weather conditions. Acceptable agreements between the predicted and measured values were obtained at this early stage of development. Further refinements in system and model validation are in progress in order to improve the accuracy of the predictions. Ultimately, as the final product of this investigation, this model will be used to predict the performance of solar domestic hot water and space heating systems in different Canadian locations, different operating conditions and water draw schedules.Copyright

GEM green energy and mobility: The case of Canada and Italy

At the present time, the principle topics for the modern world are the reduction of environmental pollution and the production of energy from renewable sources such as for example solar photovoltaic systems, wind farms, hydroelectric plants, etc. This specific care is determined by different reasons, in particular from the Kyoto Protocol, and from various problems resulting from the use of fossil fuels, like greenhouse effect, acid rain and climate change. The scope of this work is to study, for different years in Italy and Canada, the production of electric energy from renewables. At a later stage, the attention will be given to the distribution of the electric energy consumption for different sectors. In particular, the possible solutions are investigated in order to reduce pollutant emissions. This paper examines the integration of renewable energy sources in Canada and Italy where the attention is on the possibility to reduce emissions when integrating electric vehicles.