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<p>The need for renewable energy is underscored by the shift from fossil fuels without compromising economic growth. The European Union’s strategy for resilient energy recognizes solar energy as a crucial resource. In recent years, there has been a significant focus on evaluating renewable energy production, distribution, consumption, storage, and applications. One key aspect of this evaluation is exploring production capacity trends using forecasting models. Our study proposes a space–time forecasting approach using the Curve Fit Forecast tool integrated in ArcGIS Pro 3.3, applied to solar energy in 37 European locations based on 1990–2022 data. The model tested four curved types and identified exponential growth patterns in 54% of cases, with consistent upward trends confirmed through RMSE validation. Outlier detection and 3D spatial–temporal visualization provide diagnostic insight into regional disparities and transformation regimes. This digitally enabled, geostatistical method aligns with the EU’s data-driven energy transition agenda, offering a scalable and interpretable tool for policymakers and planners.</p>

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Solar energy production in EU + countries using space–time forecasting

  • 期刊论文

  • 发表日期：

    2026年1月9日

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  • Adriana Grigorescu,
  • Cristina Lincaru,
  • Camelia Speranta Pirciog

摘要

The need for renewable energy is underscored by the shift from fossil fuels without compromising economic growth. The European Union’s strategy for resilient energy recognizes solar energy as a crucial resource. In recent years, there has been a significant focus on evaluating renewable energy production, distribution, consumption, storage, and applications. One key aspect of this evaluation is exploring production capacity trends using forecasting models. Our study proposes a space–time forecasting approach using the Curve Fit Forecast tool integrated in ArcGIS Pro 3.3, applied to solar energy in 37 European locations based on 1990–2022 data. The model tested four curved types and identified exponential growth patterns in 54% of cases, with consistent upward trends confirmed through RMSE validation. Outlier detection and 3D spatial–temporal visualization provide diagnostic insight into regional disparities and transformation regimes. This digitally enabled, geostatistical method aligns with the EU’s data-driven energy transition agenda, offering a scalable and interpretable tool for policymakers and planners.