Electricity Load and Peak Forecasting: Feature Engineering, Probabilistic LightGBM and Temporal Hierarchies

被引：0

作者：

Rubattu, Nicolo ^{[1
]}

Maroni, Gabriele ^{[1
]}

Corani, Giorgio ^{[1
]}

机构：

[1] Dalle Molle Inst Artificial Intelligence IDSIA, USI SUPSI, CH-6962 Lugano, Switzerland

来源：

ADVANCED ANALYTICS AND LEARNING ON TEMPORAL DATA, AALTD 2023 | 2023年 / 14343卷

基金：

瑞士国家科学基金会;

关键词：

Load Forecasting; Feature engineering; Gradient Boosting; Hierarchical Forecasting; Forecast Reconciliation; MODEL;

D O I：

10.1007/978-3-031-49896-1_18

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We describe our experience in developing a predictive model that placed a high position in the BigDEAL Challenge 2022, an energy competition of load and peak forecasting. We present a novel procedure for feature engineering and feature selection, based on cluster permutation of temperatures and calendar variables. We adopted gradient boosting of trees and we enhanced its capabilities with trend modeling and distributional forecasts. We also included an approach to forecasts combination known as temporal hierarchies, which further improves the accuracy.

引用

页码：276 / 292

页数：17

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