AnIO: anchored input–output learning for time-series forecasting

被引：0
作者：
Ourania Stentoumi
Paraskevi Nousi
Maria Tzelepi
Anastasios Tefas
机构：
[1] Aristotle University of Thessaloniki,Department of Informatics
[2] ETH Zürich and EPFL,Swiss Data Science Center
[3] CERTH,ITI
来源：
Neural Computing and Applications | 2024年 / 36卷
关键词：
Time-series forecasting; Electric load demand forecasting; Anchored input–output learning; Deep learning; Greek energy market;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
In this work, the short-term electric load demand forecasting problem is addressed, proposing a method inspired by the use of anchors in object detection methods. Specifically, a method named Anchored Input–Output Learning (AnIO) is proposed. AnIO proposes to define and use an anchor, reformulating the problem into offset prediction instead of actual load value prediction. Additionally, the use of anchor-encoded input features to match the encoded output is proposed. Extensive experiments were conducted, considering different anchors and model architectures on different datasets. Considering the Greek energy market, AnIO improves the performance from 2.914 to 2.251% in terms of MAPE. In conclusion, AnIO method achieves to improve the performance, considering time-series forecasting tasks.
引用
收藏
页码：2683 / 2693
页数：10
相关论文
共 97 条
[1]  
Docheshmeh Gorgij A(2022)Drought modelling by standard precipitation index (SPI) in a semi-arid climate using deep learning method: long short-term memory Neural Comput Appl 34 21911-21925
[2]  
Alizamir M(2022)On the forecasting of multivariate financial time series using hybridization of DCC-GARCH model and multivariate ANNs Neural Comput Appl 31 2217-2231
[3]  
Kisi O(2019)Forecasting of turkey’s monthly electricity demand by seasonal artificial neural network Neural Comput Appl 33 301-320
[4]  
Elshafie A(2021)Multi-step least squares support vector machine modeling approach for forecasting short-term electricity demand with application Neural Comput Appl 34 10533-10545
[5]  
Fatima S(2022)A deep LSTM network for the Spanish electricity consumption forecasting Neural Comput Appl 34 477-491
[6]  
Uddin M(2022)An adaptive backpropagation algorithm for long-term electricity load forecasting Neural Comput Appl 32 914-938
[7]  
Hamzaçebi C(2016)Probabilistic electric load forecasting: a tutorial review Int J Forecast 10 71054-71090
[8]  
Es HA(2022)Load forecasting techniques for power system: research challenges and survey IEEE Access 7 1-19
[9]  
Çakmak R(2020)Electricity load forecasting: a systematic review J Electr Syst Inf Technol 54 799-805
[10]  
Li R(2003)Short-term electricity demand forecasting using double seasonal exponential smoothing J Oper Res Soc 275 2467-2478
12345678910