期刊论文详细信息
Energy Informatics
Adequacy of neural networks for wide-scale day-ahead load forecasts on buildings and distribution systems using smart meter data
article
Valgaev, Oleg1  Kupzog, Friederich1  Schmeck, Hartmut2 
[1] Austrian Institute of Technology;Karlsruhe Institute of Technology
关键词: Smart grid;    Machine learning;    Load forecasting;    Day-ahead;    Neural network;    Local load;    Buildings;    Microgrids;    Distribution system;   
DOI  :  10.1186/s42162-020-00132-6
来源: Springer
PDF
【 摘 要 】

Power system operation increasingly relies on numerous day-ahead forecasts of local, disaggregated loads such as single buildings, microgrids and small distribution system areas. Various data-driven models can be effective predicting specific time series one-step-ahead. The aim of this work is to investigate the adequacy of neural network methodology for predicting the entire load curve day-ahead and evaluate its performance for a wide-scale application on local loads. To do so, we adopt networks from other short-term load forecasting problems for the multi-step prediction. We evaluate various feed-forward and recurrent neural network architectures drawing statistically relevant conclusions on a large sample of residential buildings. Our results suggest that neural network methodology might be ill-chosen when we predict numerous loads of different characteristics while manual setup is not possible. This article urges to consider other techniques that aim to substitute standardized load profiles using wide-scale smart meters data.

【 授权许可】

CC BY   

【 预 览 】
附件列表
Files Size Format View
RO202108110000054ZK.pdf 953KB PDF download
  文献评价指标  
  下载次数:3次 浏览次数:1次