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In the effort to achieve carbon neutrality through a decentralizedelectricity market, accurate short-term load forecasting at low aggregationlevels has become increasingly crucial for various market participants'strategies. Accurate probabilistic forecasts at low aggregation levels canimprove peer-to-peer energy sharing, demand response, and the operation ofreliable distribution networks. However, these applications require not onlyprobabilistic demand forecasts, which involve quantification of the forecastuncertainty, but also determining which consumers to include in the aggregationto meet electricity supply at the forecast lead time. While research papershave been proposed on the supply side, no similar research has been conductedon the demand side. This paper presents a method for creating a portfolio thatoptimally aggregates demand for a given energy demand, minimizing forecastinaccuracy of overall low-level aggregation. Using probabilistic load forecastsproduced by either ARMA-GARCH models or kernel density estimation (KDE), wepropose three approaches to creating a portfolio of residential households'demand: Forecast Validated, Seasonal Residual, and Seasonal Similarity. Anevaluation of probabilistic load forecasts demonstrates that all threeapproaches enhance the accuracy of forecasts produced by random portfolios,with the Seasonal Residual approach for Korea and Ireland outperforming theothers in terms of both accuracy and computational efficiency.

Probabilistic Forecast-based Portfolio Optimization of Electricity Demand at Low Aggregation Levels