A review of solar and wind energy forecasting: From single-site to multi-site paradigm
The global energy transition is no longer a distant vision — it’s unfolding now, rapidly, and with it comes a crucial question: how do we predict the unpredictable? When it comes to solar and wind energy, forecasting isn’t just a technical detail; it’s a keystone of modern energy systems. In a new paper just published in Applied Energy, Alessio Verdone, Massimo Panella, Enrico De Santis, and Antonello Rizzi from Sapienza University of Rome offer a timely and in-depth exploration of how forecasting methods have evolved to meet this challenge.
Their work is more than just a literature review. It’s a methodological study around the transformation of forecasting paradigms, tracing the field’s progress from early single-site statistical models to the latest deep learning architectures that analyze spatio-temporal data from entire networks of plants. The authors shed light on how our understanding — and our tools — have shifted alongside the growing complexity of renewable energy infrastructures.
At the heart of the paper is a simple but powerful idea: renewable energy production is no longer a local matter. Today’s systems consist of distributed solar panels and wind farms spread across vast areas. By treating each site in isolation, we miss the chance to capture valuable correlations between them. This is where multi-site forecasting comes into play, allowing models to learn not just from the past of a single plant, but from the coordinated behavior of many. And thanks to innovations in machine learning—particularly Graph Neural Networks, Transformers, and hybrid architectures — we now have the tools to make this possible.
The paper is rich with insights. It offers a structured classification of forecasting methods and benchmarks, highlights the most commonly used datasets (and the difficulty in accessing reliable public data), and discusses the metrics used to evaluate performance. But what makes this work stand out is the authors’ critical perspective. They don’t just describe methods — they ask what works, what doesn’t, and why. Their analysis of how spatial and temporal data can be integrated to boost performance speaks directly to current needs in grid management and renewable energy communities.
For researchers, engineers, and energy planners, this review is a valuable resource. It connects the dots between methodological innovation and practical application, offering a clear picture of where the field stands and where it’s heading. More importantly, it invites readers to think systemically: to see renewable energy forecasting not as a single algorithmic task, but as a complex, multi-layered problem with implications for sustainability, policy, and technology.
If you’re interested in the intersection of AI and energy, or if you’re working on Smart Grids or Renewable Energy Communities, forecasting tools, or the design of future energy systems, this is a paper worth diving into.
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