Scalable quantile predictions of peak loads for non-residential customer segments

TL;DR

This paper introduces a probabilistic extension of Velander's formula for peak load prediction, enabling accurate year-ahead quantile forecasts for non-residential customers and their aggregations, improving grid congestion management.

Contribution

It develops a quantile regression approach for Velander's formula, addressing its limitations by incorporating probabilistic predictions and non-crossing constraints.

Findings

Aggregations have larger peak loads than individual customers.

The method accurately predicts peak loads year ahead.

It effectively models diverse customer consumption patterns.

Abstract

Electrical grid congestion has emerged as an immense challenge in Europe, making the forecasting of load and its associated metrics increasingly crucial. Among these metrics, peak load is fundamental. Non-time-resolved models of peak load have their advantages of being simple and compact, and among them Velander's formula (VF) is widely used in distribution network planning. However, several aspects of VF remain inadequately addressed, including year-ahead prediction, scaling of customers, aggregation, and, most importantly, the lack of probabilistic elements. The present paper proposes a quantile interpretation of VF that enables VF to learn truncated cumulative distribution functions of peak loads with multiple quantile regression under non-crossing constraints. The evaluations on non-residential customer data confirmed its ability to predict peak load year ahead, to fit customers with a wide range of electricity consumptions, and to model aggregations of customers. A noteworthy finding is that for a given electricity consumption, aggregations of customers have statistically larger peak loads than a single customer.