TY - JOUR
T1 - Fine-Grained Peak Load Prediction Leveraging Generalized Additive Models and Deep Neural Network Architectures
AU - George Brown
AU - Michael Taylor
AU - Sarah Wilson
AU - Olivia Harris
JF - Journal of Smart Grids, Energy Analytics and Optimization
JO - J Smart Grids Energy Anal Optim
Y1 - 2025
VL - 4
IS - 1
SP - 107
N2 - This study investigates the prediction of fine-grained electricity peak demand using coarser-resolution data. Specifically, it addresses whether sparse high-resolution monitoring can inform forecasts of future high-resolution peaks when detailed measurements are unavailable, a question of particular importance for network operators evaluating the replacement of costly monitoring systems with predictive models. We develop models capable of estimating half-hourly maxima and minima from one-minute interval electricity load data using inputs aggregated to 30-minute intervals. Our methodology combines generalized additive models (GAMs) and deep neural networks (DNNs), both widely applied in load forecasting. The analysis includes an in-depth assessment of input feature importance, focusing on load patterns, meteorological variables, and seasonal trends. The proposed approach achieved first place in a data competition organized by Western Power Distribution (WPD), a UK electricity distribution operator. Beyond the competition, we conduct an extended evaluation over an additional 11 months, confirming that our combined model significantly outperforms the benchmark in out-of-sample root mean squared error (RMSE), achieving a 57.4% reduction compared to the competition reference.
UR - https://isespub.com/k825976775
ER -