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

Abstract

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.

Machine learning forecasting of solar PV production using single and hybrid models over different time horizons

“…An RMSE of 1.5565 was achieved by Ref. [ 19 ] when ANN was used to forecast solar PV production days ahead. Similarly, using solar irradiation, ambient temperature and model temperature as input variables, the study in Ref.…”

Evaluating the performance of the Anwaralardh photovoltaic power generation plant in Jordan: Comparative analysis using artificial neural networks and multiple linear regression modeling

“…However, ANN is usually considered the "black box" and it is difficult to comprehend their results (Koeppe et al, 2021). Another popular statistical approach in modeling PV power generation is the multiple linear regression (MLR) model (EL-AAL et al, 2023;Yadav et al, 2022;Thombare et al, 2022;Limouni et al 2022). In comparison with ANN, the MLR model is relatively simple and easy to implement.…”

Comparison and Statistical analysis of various machine learning techniques for daily prediction of solar GHI representing India’s overall solar radiation

“…The ANN model outperformed all the other models with R 2 = 0.9911. (Yadav et al 2022) predicted solar power for Malaysian and Indian regions using the ANN model, and RMSE was considered the primary evaluation parameter. (Jebli et al 2021) utilized the Pearson correlation coefficient for solar energy prediction using SVM, LR, ANN, and RF ML models.…”