Efficient mid-term forecasting of hourly electricity load using generalized additive models

ArXiv ID: 2405.17070 “View on arXiv”

Authors: Unknown

Abstract

Accurate mid-term (weeks to one year) hourly electricity load forecasts are essential for strategic decision-making in power plant operation, ensuring supply security and grid stability, planning and building energy storage systems, and energy trading. While numerous models effectively predict short-term (hours to a few days) hourly load, mid-term forecasting solutions remain scarce. In mid-term load forecasting, capturing the multifaceted characteristics of load, including daily, weekly and annual seasonal patterns, as well as autoregressive effects, weather and holiday impacts, and socio-economic non-stationarities, presents significant modeling challenges. To address these challenges, we propose a novel forecasting method using Generalized Additive Models (GAMs) built from interpretable P-splines that is enhanced with autoregressive post-processing. This model incorporates smoothed temperatures, Error-Trend-Seasonal (ETS) modeled and persistently forecasted non-stationary socio-economic states, a nuanced representation of effects from vacation periods, fixed date and weekday holidays, and seasonal information as inputs. The proposed model is evaluated using load data from 24 European countries over more than 9 years (2015-2024). This analysis demonstrates that the model not only has significantly enhanced forecasting accuracy compared to state-of-the-art methods but also offers valuable insights into the influence of individual components on predicted load, given its full interpretability. Achieving performance akin to day-ahead Transmission System Operator (TSO) forecasts, with computation times of just a few seconds for several years of hourly data, underscores the potential of the model for practical application in the power system industry.

Keywords: Electricity Load Forecasting, Generalized Additive Models (GAMs), P-splines, Mid-Term Forecasting, Energy Markets, Commodities (Electricity)

Complexity vs Empirical Score

• Math Complexity: 4.5/10
• Empirical Rigor: 8.5/10
• Quadrant: Street Traders
• Why: The paper employs interpretable Generalized Additive Models (GAMs) with P-splines, which is a standard but moderately complex statistical technique, and focuses heavily on practical application with extensive multi-country, multi-year backtesting, performance benchmarks against TSO forecasts, and computational efficiency.

  flowchart TD
    A["Research Goal<br>Develop Accurate &<br>Interpretable Mid-Term<br>Hourly Load Forecasting"] --> B["Data Collection & Input<br>9+ Years (2015-24)<br>24 European Countries"]
    
    B --> C{"Core Methodology<br>Generalized Additive Models with P-Splines"}
    
    C --> D["Input Features Processing"]
    
    D --> E["Smoothed Temperatures"]
    D --> F["ETS Modeled Socio-Economic<br>States (Forecasted persistently)"]
    D --> G["Holiday Effects<br>Fixed date & Weekday"]
    D --> H["Seasonal Information<br>Daily, Weekly, Annual"]
    
    E & F & G & H --> C
    
    C --> I["Autoregressive<br>Post-Processing"]
    
    I --> J["Key Outcomes & Findings"]
    
    J --> K["Significantly Enhanced<br>Forecasting Accuracy"]
    J --> L["Full Interpretability<br>Insights into individual<br>component impacts"]
    J --> M["Computational Efficiency<br>Seconds for years of<br>hourly data"]
    J --> N["Performance comparable<br>to Day-Ahead TSO Forecasts"]
    
    style A fill:#e1f5fe
    style J fill:#f3e5f5
    style C fill:#fff3e0