High-Resolution Peak Demand Estimation Using Generalized Additive Models and Deep Neural Networks

TL;DR

This paper introduces a combined modeling approach using GAM and DNN to accurately predict high-resolution electricity peak demand from lower-resolution data, aiding network operators in cost-effective monitoring.

Contribution

It presents a novel combination of GAM and DNN models for high-resolution load prediction from lower-resolution inputs, outperforming existing benchmarks.

Findings

Proposed models reduce out-of-sample RMSE by 57.4% compared to benchmarks.

Models effectively capture load, weather, and seasonal effects.

The approach won a data competition and demonstrated robustness over multiple months.

Abstract

This paper covers predicting high-resolution electricity peak demand features given lower-resolution data. This is a relevant setup as it answers whether limited higher-resolution monitoring helps to estimate future high-resolution peak loads when the high-resolution data is no longer available. That question is particularly interesting for network operators considering replacing high-resolution monitoring predictive models due to economic considerations. We propose models to predict half-hourly minima and maxima of high-resolution (every minute) electricity load data while model inputs are of a lower resolution (30 minutes). We combine predictions of generalized additive models (GAM) and deep artificial neural networks (DNN), which are popular in load forecasting. We extensively analyze the prediction models, including the input parameters' importance, focusing on load, weather, and seasonal effects. The proposed method won a data competition organized by Western Power Distribution, a British distribution network operator. In addition, we provide a rigorous evaluation study that goes beyond the competition frame to analyze the models' robustness. The results show that the proposed methods are superior to the competition benchmark concerning the out-of-sample root mean squared error (RMSE). This holds regarding the competition month and the supplementary evaluation study, which covers an additional eleven months. Overall, our proposed model combination reduces the out-of-sample RMSE by 57.4\% compared to the benchmark.