Strategies for designing machine learning models in renewable energy with insufficient data

In the area of renewable energy including wind, solar, and electricity, Machine Learning has established a far-reaching conceptual and algorithmic design framework. However, the design of Machine Learning architectures typically relies on substantial amounts of data. The data acquisition process often involves complex tasks of gathering, cleaning, and labeling data. This challenge is exacerbated in the energy sector due to concerns related to data security and the uniqueness of development initiatives, leading to a potential scarcity of available data. Quite commonly, researchers encounter a vast array of innovative projects that have not been studied in the past and therefore only limited data become available. Therefore, there is an urgent and highly motivated need to develop machine learning strategies that can construct reliable models in the presence of limited data. In this study, three machine learning models tailored for small-scale data scenarios are developed and analyzed. The first model, a fuzzy cluster ensemble approach, focuses on utilizing small training samples treated as the core of clustering. By applying clustering algorithms, one derives membership functions reflecting the structure of data that facilitate accurate predictions. The second model, a kernel integration framework, combines kernel functions with limited training data to calculate activation levels, enabling accurate prediction outputs. For both models, the final predictions are generated by applying weighted aggregations, where the weights are derived from the respective membership grades and activation levels of the data. Additionally, a neural network contrastive learning model is introduced, which enhances the training process by employing pairwise comparisons among data points. This method effectively increases the amount of useable training data, improving the model's ability to make accurate predictions. Together, these approaches provide a robust solution for making reliable predictions even in data-scarce scenarios within renewable energy research. Moreover, the predictive performance of these models is quantified using the Root Mean Square Error criterion. This study also assesses the performance of the developed models by forming a granular characterization of results delivered by the designed models. A comprehensive suite of experimental studies is included, accompanied by an exhaustive comparative analysis. Compared to conventional machine learning models, especially neural networks, the designed models offer reliable results even with limited training data, showing an improvement of around 20 %, thus laying the groundwork for digitization processes in the realm of sustainable energy.