This study aims to point out accurate machine learning (ML) prediction methods to forecast solar energy generation. We analyze a dataset with 8,760 rows of data and 6 variables: Wind Speed
An integrated machine learning model and the statistical approach are used to anticipate future solar power generation from renewable energy plants. This hybrid model improves accuracy by integrating machine
In the context of escalating concerns about environmental sustainability in smart cities, solar power and other renewable energy sources have emerged as pivotal players in the global effort to curtail greenhouse gas
Solar power, also known as solar electricity, is the conversion of energy from sunlight into electricity, either directly using photovoltaics (PV) or indirectly using concentrated solar power. Solar panels use the photovoltaic effect to convert
the intricate patterns and complex relationships inherent in solar power generation data. The developed machine learning models can aid solar PV investors in streamlining their processes
By incorporating machine learning-based approaches into the realm of solar power generation forecasting, researchers have unlocked the potential to harness solar energy resources more effectively. These
Photovoltaic systems have become an important source of renewable energy generation. Because solar power generation is intrinsically highly dependent on weather fluctuations, predicting power generation using
for solar power generation as in solar power forecasting is required for electric grid. Solar power generation is weather-dependent and unpredictable, this forecast is complex and difficult. The
Estimating energy production from solar panels with machine learning methods will make positive contributions by guiding the investments to be made for the installation of solar power plants (SPP) (Gürtürk et al. 2022). Artificial intelligence and machine learning methods studies on solar energy systems in the literature are given in Table 1.
For reliable predictions of solar electricity generation, one must take into consideration changes in weather patterns over time. In this paper, a hybrid model that integrates machine learning and statistical approaches is suggested for predicting future solar energy generation.
As can be seen from the table, ANN and SVM methods are more widely used than other methods. In contrast, the AdaBoost method is the least common. In the literature review, the SGD model was not found in the studies of machine learning methods in solar energy systems.
However, this research aims to enhance the efficiency of solar power generation systems in a smart grid context using machine learning hybrid models such as Hybrid Convolutional-Recurrence Net (HCRN), Hybrid Convolutional-LSTM Net (HCLN), and Hybrid Convolutional-GRU Net (HCGRN).
Global renewable transition: As AI and machine learning enhance the efficiency and reliability of solar energy systems, they will contribute significantly to the global transition to 100 % renewable energy. Solar power, in particular, will become a cornerstone of sustainable energy production due to these technological advancements. 4.
In the past, commonly used machine learning models for predicting solar PV power production included support vector machine (SVM), K-nearest neighbors (K-NN) , and artificial neural networks (ANNs) . These statistical models mainly rely on historical data to predict future time series.
