Machine Learning Based Prediction of Output PV Power in India and Malaysia with the Use of Statistical Regression.

Climate change and pollution are serious issues that are driving people to adopt renewable energy instead of fossil fuels. Most renewable energy technologies rely on atmospheric conditions to generate power. Solar energy is a renewable energy source that causes the least environmental damage. Solar energy can be converted to electricity, which necessitates the use of a PV system. This study presents a design, which analyses the output power performance of PV, using machine learning technique in India and Malaysia; using this, we would get the predicted amount of solar power using different weather conditions for both India and Malaysia. This study is divided into two sections, such as the data collection section and the implementation system. Dataset was collected from a weather NASA website, which took various weather parameters, based on which the model will be evaluated. The proposed research work is developed using ANN and is an amalgamation of statistical regression and neural networks, which help the model to get high accuracy by helping the model learn more complex relationships between parameters, which is able to evaluate the output power performance of photovoltaic cells with different environmental condition parameters in India and Malaysia. The ANN models are found to successfully predict PV output power with root mean square error (RMSE) of 1.5565, which was used as a measure of our model's accuracy. This ANN model also outperforms other models available in the literature. This will have a noteworthy contribution in scaling the PV deployment in countries such as India and Malaysia and will increase the share of PV power in their national power production, as it would give the industry and the two countries an idea as to how the predicted output PV power would vary based on weather conditions, such as temperature. [ABSTRACT FROM AUTHOR]