PHOTOVOLTAIC GENERATION FORECASTING MODELS: CONCEPTUAL ENSEMBLE ARCHITECTURES

The decisions regarding power regulation, energy resource planning, and integrating “green” energy into the electrical grid hinge on precise probabilistic forecasts. One of the potential strategies to enhance forecast accuracy is the utilization of ensemble forecasting methods. They represent an approach where multiple models collaborate to achieve superior results compared to what a single model could produce independently. These methods can be categorized into two main categories: competitive and collaborative ensembles. Competitive ensembles harness the diversity of parameters and data to create a rich pool of base models. This approach may encompass statistical analysis, noise filtering, and anomaly elimination. On the other hand, collaborative ensembles rely on the interaction among models to achieve better outcomes. These methods encompass strategies such as weighted predictions, voting, aggregation, and a combination of model results. The research of ensemble forecasting methods in the context of photovoltaic generation is highly relevant, as solar energy represents a crucial source of renewable energy. Accurate predictions of solar energy production address the challenges related to the efficient utilization of photovoltaic panels and their integration into the overall energy system. This paper investigates conceptual ensemble architectures for photovoltaic energy forecasting. These architectures encompass various methods of aggregating base models within an ensemble, allowing for the consideration of different aspects and peculiarities of solar data, such as solar irradiation intensity, meteorological conditions, geographic factors, and more. These conceptual models are developed based on well-established statistical, machine learning, and artificial intelligence methods. Therefore, this paper provides an overview of ensemble forecasting methods for renewable energy, covering competitive and collaborative ensembles, as well as developing conceptual models for solar energy forecasting. This work aims to elevate the accuracy and efficiency of forecasts in the realm of renewable energy, representing a significant step in the advancement of sustainable and environmentally friendly energy production. Keywords: probabilistic solar forecasting, ensemble model, forecast combination, competitive ensembles, collaborative ensembles, conceptual models.