A Quantum Inspired Variational Kernel and Explainable AI Framework for Cross Region Solar and Wind Energy Forecasting

TL;DR

This paper introduces a hybrid framework for solar and wind energy forecasting that combines classical models, a quantum-inspired kernel, and explainability tools, achieving high accuracy across different regions.

Contribution

It develops a novel four-stage hybrid forecasting framework integrating quantum-inspired kernels and AI explainability, improving cross-region energy prediction accuracy.

Findings

Framework stays within 1% of the best classical baseline in domain forecasting.

Quantum-inspired kernel significantly enhances weather regime discrimination.

The approach demonstrates robustness across Iberian, North Sea, and Texas energy data.

Abstract

Reliable short horizon forecasting of solar and wind generation is a structural prerequisite of any modern power system yet most published forecasters are tuned and evaluated on a single climatic regime and most algorithmic novelty has been concentrated either on classical recurrent networks or on monolithic foundation models that combine forecasting and explanation We develop a four stage hybrid framework that separates these concerns The first stage acquires hourly generation irradiance and surface weather records through public application programming interfaces The second stage trains three classical baselines autoregressive integrated moving average gradient boosted regression trees and a two layer long short term memory network and produces a strong point forecast together with a residual error series The third stage corrects the residual through a quantum inspired variational kernel built on a six qubit hardware efficient ansatz with three repeated entangling layers The fourth stage uses generative artificial intelligence strictly as an explainability layer that reads the measured benchmark numbers and produces a structured natural language interpretation Across three regions drawn from open public archives Iberian solar North Sea wind and a mixed Texas trace the proposed configuration stays within one percentage point of the strongest classical baseline on the in domain forecasting task and the quantum inspired kernel separates calm and stormy weather regimes with a Fisher discriminant ratio approximately fifteen fold higher than a tuned radial basis kernel