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Neural network model for forecasting solar energy generation and analysis of power output controlling by energy storage schemes

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dc.contributor.advisor Attalage RA
dc.contributor.author Weerasekara WBMUT
dc.date.accessioned 2019
dc.date.available 2019
dc.date.issued 2019
dc.identifier.citation Weerasekara, W.B.M.U.T. (2019). Neural network model for forecasting solar energy generation and analysis of power output controlling by energy storage schemes [Master’s theses, University of Moratuwa]. Institutional Repository University of Moratuwa. http://dl.lib.mrt.ac.lk/handle/123/16061
dc.identifier.uri http://dl.lib.mrt.ac.lk/handle/123/16061
dc.description.abstract Modeling of power fluctuations in a solar PV power plant using an Artificial Neural Network (ANN) was carried out in the study. The resulting model was used to evaluate the energy storage requirement to control fluctuations of the power output. The ANN was trained to model the output of a 300kW solar PV system installed in Colombo with an average hourly energy output of 90.55kWh and an average daily energy production of 1177 kWh. The ANN model proved to deliver forecasts with significant accuracy and generalizability. Correlation coefficients for training, validation and testing were 0.945 0.948 and 0.939 respectively. Further validation was done using an isolated data set of a time period of a month for which model was able to achieve a correlation coefficient of 0.93. Residual analysis confirmed the error was random and free of autocorrelation. Error terms had a normal distribution with mean 1.09kWh and standard deviation of 20.06kWh. A direct mapping was established between meteorological parameters and power output of a solar PV system, as oppose to estimating solar irradiance. Energy storage requirement was evaluated for two power output control schemes. First scheme specifies a ramp up, ramp down rate, and a continuous power delivery period. By means of an optimizing algorithm the combination of parameters corresponding to the least energy storage requirement was established and the result for energy requirement was approximately 15% of the average daily energy generation of the PV system. Variation of energy storage requirement under different operating conditions was analyzed further. The second output control scheme uses moving average smoothening to control power output. The calculated energy storage requirement for moving average scheme was approximately 8% of the average daily energy generation. The effect of imposing restriction on operating parameters of the schemes were examined in detail. en_US
dc.language.iso en en_US
dc.subject MECHANICAL ENGINEEERING-Dissertations en_US
dc.subject ENERGY TECHNOLOGY-Dissertations en_US
dc.subject SOLAR ENERGY en_US
dc.subject NEURAL NETWORKS en_US
dc.subject ENERGY STORAGE en_US
dc.title Neural network model for forecasting solar energy generation and analysis of power output controlling by energy storage schemes en_US
dc.type Thesis-Abstract en_US
dc.identifier.faculty Engineering en_US
dc.identifier.degree M. Eng in Energy Technology en_US
dc.identifier.department Department of Mechanical Engineering en_US
dc.date.accept 2019
dc.identifier.accno TH3953 en_US


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