Paper: arXiv 2305.09474

Authors: Jungyeon Park, Estêvão Alvarenga, Jooyoung Jeon, Ran Li, Fotios Petropoulos, Hokyun Kim, Kwangwon Ahn

Abstract

In the effort to achieve carbon neutrality through a decentralized electricity market, accurate short-term load forecasting at low aggregation levels has become increasingly crucial for various market participants’ strategies. Accurate probabilistic forecasts at low aggregation levels can improve peer-to-peer energy sharing, demand response, and the operation of reliable distribution networks. However, these applications require not only probabilistic demand forecasts, which involve quantification of the forecast uncertainty, but also determining which consumers to include in the aggregation to meet electricity supply at the forecast lead time. While research papers have been proposed on the supply side, no similar research has been conducted on the demand side. This paper presents a method for creating a portfolio that optimally aggregates demand for a given energy demand, minimizing forecast inaccuracy of overall low-level aggregation. Using probabilistic load forecasts produced by either ARMA-GARCH models or kernel density estimation (KDE), we propose three approaches to creating a portfolio of residential households’ demand: Forecast Validated, Seasonal Residual, and Seasonal Similarity. An evaluation of probabilistic load forecasts demonstrates that all three approaches enhance the accuracy of forecasts produced by random portfolios, with the Seasonal Residual approach for Korea and Ireland outperforming the others in terms of both accuracy and computational efficiency.

Complexity vs Empirical Score

  • Math Complexity: 5.5/10
  • Empirical Rigor: 6.8/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: This paper uniquely adapts portfolio optimization techniques to the demand side of electricity markets using probabilistic forecasting frameworks like ARMA-GARCH and KDE. It features solid empirical testing across multiple national datasets (Korea and Ireland), demonstrating notable improvements over random portfolio baseline benchmarks.

Research Flowchart

  flowchart TD
    A[Goal: Minimize forecast uncertainty in low-level demand aggregation] --> B[Input Data: Residential Household Load Profiles]
    B --> C[Generate Probabilistic Load Forecasts<br>via ARMA-GARCH or KDE]
    C --> D[Portfolio Optimization Approaches:<br>1. Forecast Validated<br>2. Seasonal Residual<br>3. Seasonal Similarity]
    D --> E[Evaluate Forecast Accuracy & Efficiency<br>against Random Portfolios]
    E --> F[Outcome: All approaches improve accuracy;<br>Seasonal Residual performs best]