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A Neural network based model for forecasting the power output of a commercial scale photovoltaic power plant

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dc.contributor.advisor Nissanka ID
dc.contributor.author Manchanayaka MAAP
dc.date.accessioned 2022
dc.date.available 2022
dc.date.issued 2022
dc.identifier.citation Manchanayaka, M.A.A.P. (2022). A Neural network based model for forecasting the power output of a commercial scale photovoltaic power plant [Master's theses, University of Moratuwa]. Institutional Repository University of Moratuwa. http://dl.lib.uom.lk/handle/123/20927
dc.identifier.uri http://dl.lib.uom.lk/handle/123/20927
dc.description.abstract Solar photovoltaic (PV) is penetrating electrical grids with a substantial growth of new additions, as a result of renewable energy policies and plans implemented locally and globally. However, the intermittent nature of the availability of solar energy brings an uncertainty into electrical power systems making it complex for power management and integrating into existing electricity infrastructure. This has been a key issue in promoting renewable energy in developing countries. Accurate solar power forecasts in different time horizons can play a vital role to bring down the uncertainty by a significant margin. In this work, a neural network (NN) model was coupled with a decomposition and transposition (D&T) model to forecast day(s) ahead hourly PV output of a grid connected 1 MW solar PV plant located in Hambantota, Sri Lanka. Historical weather and solar radiation data for last 14 years were collected from two APIs (Application Programming interfaces) for the location of PV plant and variation of global horizontal irradiation (GHI) with percentage cloud cover, rain, temperature, relative humidity, and wind speed were analysed. The selected parameters from the analysis together with day and hour numbers were fed in to the NN model through a scaling layer and trained it using Levenberg–Marquardt backpropagation algorithm. Optimum NN model was selected by changing the hidden layer sizes and calculating the mean squared error. The forecasted GHI values of the optimized NN model were decomposed to diffuse horizontal irradiance (DHI) and direct normal irradiance (DNI) using Erbs correlation, as the first step of D & T model. Then, DHI and DNI components were converted to global tilted irradiance (GTI) using HDKR correlation, in order to calculate solar PV output, including possible plant specific losses. The correlation coefficient (R) between GHI output and target values of the trained NN model for an unseen testing data set was observed to be 0.86. For final model, mean percentage forecasting accuracy was observed to be 86% with 12% standard deviation. The model could be adopted to any commercial or utility scale solar PV plant which is in a tropical climate region. en_US
dc.language.iso en en_US
dc.subject MACHINE LEARNING MODEL en_US
dc.subject SOLAR PV en_US
dc.subject NEURAL NETWORK en_US
dc.subject SOLAR POWER FORECAST en_US
dc.subject UTILITY SCALE POWER PLANT en_US
dc.subject ENERGY TECHNOLOGY– Dissertation en_US
dc.subject MECHANICAL ENGINEERING– Dissertation en_US
dc.title A Neural network based model for forecasting the power output of a commercial scale photovoltaic power plant 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 2022
dc.identifier.accno TH4793 en_US


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