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

共 50 条

[31] Electricity peak load forecasting with self-organizing map and support vector regression
Fan, Shu
Mao, Chengxiong
Chea, Luonan
IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING, 2006, 1 (03) : 330 - 336
[32] WINTER PEAK ELECTRICITY LOAD FORECASTING IN SOUTH AFRICA USING EXTREME VALUE THEORY
Chikobvu, D.
Sigauke, C.
Verster, A.
SOUTH AFRICAN STATISTICAL JOURNAL, 2012, 46 (02) : 377 - 393
[33] Feature selection for probabilistic load forecasting via sparse penalized quantile regression
Wang, Yi
Gan, Dahua
Zhang, Ning
Xie, Le
Kang, Chongqing
JOURNAL OF MODERN POWER SYSTEMS AND CLEAN ENERGY, 2019, 7 (05) : 1200 - 1209
[34] Structured probabilistic models for capturing household and temporal variations in the internal electricity load
Kim, Chulho
Byun, Jiwook
Go, Jaehyun
Heo, Yeonsook
ENERGY AND BUILDINGS, 2023, 279
[35] Feature selection for probabilistic load forecasting via sparse penalized quantile regression
Yi WANG
Dahua GAN
Ning ZHANG
Le XIE
Chongqing KANG
Journal of Modern Power Systems and Clean Energy, 2019, 7 (05) : 1200 - 1209
[36] Probabilistic load forecasting considering temporal correlation: Online models for the prediction of households' electrical load
Lemos-Vinasco, Julian
Bacher, Peder
Moller, Jan Kloppenborg
APPLIED ENERGY, 2021, 303
[37] Feature engineering and selection for prosumer electricity consumption and production forecasting: A comprehensive framework
Wen, Qianyun
Liu, Yang
APPLIED ENERGY, 2025, 381
[38] A Two-Stage Multistep-Ahead Electricity Load Forecasting Scheme Based on LightGBM and Attention-BiLSTM
Park, Jinwoong
Hwang, Eenjun
SENSORS, 2021, 21 (22)
[39] Feature Extraction for Day-ahead Electricity-Load Forecasting in Residential Buildings
Kychkin, Aleksey, V
Chasparis, Georgios C.
IFAC PAPERSONLINE, 2020, 53 (02): : 13094 - 13100
[40] Feature selection for daily peak load forecasting using a neuro-fuzzy system
Sung-Yong Son
Sang-Hong Lee
Kyungyong Chung
Joon S. Lim
Multimedia Tools and Applications, 2015, 74 : 2321 - 2336

← 1 2 3 4 5 →