Deep neural networks enhance photovoltaic power forecasting by leveraging multivariate time-series modelling.
The Variability of Photovoltaic Energy Production
Photovoltaic energy production is inherently variable. It depends on solar irradiance, temperature, weather conditions, and a range of environmental factors that interact in highly nonlinear ways. Managing this complexity requires tools capable of simultaneously interpreting multiple variables and transforming them into reliable time-based forecasts.
A Deep Learning Model for Multivariate Forecasting
In this context, a deep neural network–based model has been developed for multivariate prediction of photovoltaic time series. The core idea is to feed the network with a structured set of heterogeneous quantities (meteorological data, environmental parameters, and production measurements) organised within a temporal window designed to capture short- and medium-term dynamics. The architecture is built to learn complex relationships among correlated inputs, moving beyond traditional univariate approaches.
Layered Architecture and Training Strategy
The model structure integrates multiple hidden layers, enabling the progressive extraction of increasingly abstract representations of the input signals. The training phase relies on real-world datasets collected from photovoltaic plants, with careful separation between training and validation data. The objective is not only to minimise average error, but also to ensure predictive stability under sudden variations in environmental conditions.
Multidimensional Representation of Time Series
Special attention is devoted to constructing the multidimensional input. Time series are not treated as simple linear sequences but as matrices of interdependent information, in which each time step encapsulates an overall energetic and climatic state. This allows the network to identify recurring patterns, seasonality effects, and latent correlations among physical variables.
Quantitative Evaluation and Performance Gains
System performance is evaluated using standard quantitative metrics commonly adopted in the energy domain, comparing forecasts with actual production values. The results show a significant reduction in prediction error compared to simpler models, highlighting the deep network’s ability to adapt to the highly nonlinear nature of the phenomenon.
Toward Data-Driven Renewable Energy Systems
The proposed framework goes beyond theoretical modelling and fits into a concrete application scenario: intelligent management of photovoltaic plants and microgrids. More accurate power forecasts enable better storage optimisation, improved grid exchange planning, and enhanced overall system efficiency. Looking ahead, this type of approach represents a step toward increasingly data-driven energy infrastructures, where artificial intelligence does not merely analyse the past but becomes an operational tool for shaping the future of renewable energy production.
Autori
F. Succetti, A. Rosato, R. Araneo, M. Panella,
