Cubist Regression, Random Forest and Support Vector Regression for Solar Power Prediction

At a time when the energy transition is inescapable and artificial intelligence is rapidly advancing in all directions, solar renewable energy output forecasting is becoming a popular concept, especially with the availability of large data sets and the critical requirement to forecast these energies, known to have a random nature. Therefore, the main goal of this study is to investigate and exploit artificial intelligence's revolutionary potential for the prediction of the electricity generated by solar photovoltaic panels. The main algorithms that will be studied in this article are cubist regression, random forest and support vector regression. This forecast is beneficial to both providers and consumers, since it will enable for more efficient use of solar renewable energy supplies, which intermittency makes their integration into the existing electrical networks a challenging task

Principal Component Analysis and Artificial Intelligence Approaches for Solar Photovoltaic Power Forecasting

In recent years, renewable energy sources have experienced remarkable growth. However, their spatial and temporal diversity makes their large-scale integration into the current power grids difficult, as the balance between the electricity output and the consumption must be maintained at all times. Therefore, it is important to focus on the resources forecast to enhance the integration of renewable energy sources, such as solar in this study. In this article, a comparative analysis of two main machine learning methods was conducted for the prediction of the hourly photovoltaic output power. Furthermore, since various factors, such as climate variables, can impact the solar photovoltaic power and complicate the prediction process, the principal component analysis was employed to investigate the interactions between the multiple predictors and minimize the dimensionality of the datasets. The prevalent factors were then used in the predictive models as inputs. This field research is very crucial because the higher the prediction accuracy, the greater the profit for energy dealers and the lower the costs for customers

Principal Component Analysis and Machine Learning Approaches for Photovoltaic Power Prediction: A Comparative Study

Nowadays, in the context of the industrial revolution 4.0, considerable volumes of data are being generated continuously from intelligent sensors and connected objects. The proper understanding and use of these amounts of data are crucial levers of performance and innovation. Machine learning is the technology that allows the full potential of big datasets to be exploited. As a branch of artificial intelligence, it enables us to discover patterns and make predictions from data based on statistics, data mining, and predictive analysis. The key goal of this study was to use machine learning approaches to forecast the hourly power produced by photovoltaic panels. A comparison analysis of various predictive models including elastic net, support vector regression, random forest, and Bayesian regularized neural networks was carried out to identify the models providing the best predicting results. The principal components analysis used to reduce the dimensionality of the input data revealed six main factor components that could explain up to 91.95% of the variation in all variables. Finally, performance metrics demonstrated that Bayesian regularized neural networks achieved the best results, giving an accuracy of R2 = 99.99% and RMSE = 0.002 kW