Solar Power Forecasting Using ML-Model

Abstract

The increased competitiveness of solar PV panels as a renewable energy source has increased the number of PV panel installations in recent years. In the meantime, higher availability of data and computational power have enabled machine learning algorithms to perform improved predictions. As the need to predict solar PV energy output is essential for many actors in the energy industry, machine learning and time series models can be employed towards this end. In this study, a comparison of different machine learning techniques and time series models is performed across five different sites in Sweden. We find that employing time series models is a complicated procedure due to the non-stationary energy time series. In contrast, machine learning techniques were more straightforward to implement. In particular, we find that the Artificial Neural Networks and Gradient Boosting Regression Trees perform best on average across all sites.

Estimation of solar-powered energy is becoming an important issue in relation to environmentally friendly energy sources, and machine learning algorithms play an important role in this area. Sunlight-based energy estimation can be viewed as a period series waiting problem, using standardized data. In addition, energy determination based on sunlight can be obtained from the Mathematical Climate Assessment Model (NWP).

Our purpose is centered around the final approach. We focus on the concept of sunlight based energy from the NWP registered with the GEFS, the Global Ensemble Forecast System, which assesses weather factors to focus on in the matrix. In this case, it would be helpful to know how estimation accuracy improves based on the size of the lattice hubs used in conjunction with AI methods. AI (ML) calculations have shown exceptional results over time, which can be used as model data sources to predict lightning with weather conditions. Use of various AI, Deep Learning and Simulated Brain Network methods for solar based energy decisions. Here is the relapse model featuring Machine Resistor Assist Vector, Anomalous Forest Area Registrar and Straight Relax Model from AI Techniques, of which the Arbitrary Backwoods Resistor beats the other two Relax Models with incredible accuracy.