Paper: arXiv 2408.00507

Authors: Monika Zimmermann, Florian Ziel

Abstract

Accurate forecasts of the impact of spatial weather and pan-European socio-economic and political risks on hourly electricity demand for the mid-term horizon are crucial for strategic decision-making amidst the inherent uncertainty. Most importantly, these forecasts are essential for the operational management of power plants, ensuring supply security and grid stability, and in guiding energy trading and investment decisions. The primary challenge for this forecasting task lies in disentangling the multifaceted drivers of load, which include national deterministic (daily, weekly, annual, and holiday patterns) and national stochastic weather and autoregressive effects. Additionally, transnational stochastic socio-economic and political effects add further complexity, in particular, due to their non-stationarity. To address this challenge, we present an interpretable probabilistic mid-term forecasting model for the hourly load that captures, besides all deterministic effects, the various uncertainties in load. This model recognizes transnational dependencies across 24 European countries, with multivariate modeled socio-economic and political states and cross-country dependent forecasting. Built from interpretable Generalized Additive Models (GAMs), the model enables an analysis of the transmission of each incorporated effect to the hour-specific load. Our findings highlight the vulnerability of countries reliant on electric heating under extreme weather scenarios. This emphasizes the need for high-resolution forecasting of weather effects on pan-European electricity consumption especially in anticipation of widespread electric heating adoption.

Complexity vs Empirical Score

  • Math Complexity: 6.0/10
  • Empirical Rigor: 8.5/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper provides a highly rigorous probabilistic mid-term load forecasting model across 24 European countries using Generalized Additive Models (GAMs) and vector autoregressive state models. It excels in empirical evaluation over a multi-year period (2015-2024), effectively incorporating spatial weather and non-stationary socio-economic/political risks.

Research Flowchart

  flowchart TD
    A["Research Goal: Probabilistic Mid-Term Electricity Load Forecasting with Spatial & Political Risks"] --> B["Data Inputs: European Weather, Socio-Economic, Political & Historical Hourly Load (24 Countries)"]
    B --> C["Methodology: Generalized Additive Models (GAMs) & Multivariate Dependencies"]
    C --> D["Computational Process: Disentangle Deterministic, Weather, Autoregressive & Spatial Non-Stationary Drivers"]
    D --> E["Findings: High Vulnerability in Electric-Heating Reliant Countries Under Extreme Weather"]
    D --> F["Outcomes: Interpretable Hourly Probabilistic Forecasts & Grid Stability Insights"]