Empirical Validation of Photo-voltaic Power Output Forecasting Methods

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The electricity produced by renewable energy sources (RES) is constantly increasing worldwide thanks to government policies and technological advancements. Photo voltaic cells are

actually a fast rising technology as the source of its power is a commodity that is abundant in

this part of the hemisphere. Uncertainty also dominates in the area of the conditions at which

the photovoltaic cells are operating, their optimal weather conditions and the effect of the

conditions on their output. In this project the existing forecasting techniques for generation

and consumption were studied so that they can be used in the multi-agent power grid control

strategy, which will be developed in the upcoming tasks. The type of Forecasting method

used in the project was the Numerical Weather Predictions model which uses meteorological

data such as Irradiance, Ambient Temperature and with the aid of Computer models such as

Artificial Neural Networks to model the power outputs of PV systems. These forecasts can be

used to enhance knowledge in the area of power allocation in PV systems as a form of

Renewable Power sources. The power outputs used in the project i.e Measured Power output

and the Predicted Power Outputs were quite similar which means this model can be safely

used as a mode of determining the amount of PV power required for a location and the power

it is likely to produce at different times. The project concluded that this form of forecast

actually produced values with high correlation values which means it can be used practically

as a form of power sharing when PV systems are used as grided form of electricity

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Empirical Validation of Photo-voltaic Power Output Forecasting Methods

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