Short-Term Load Forecasting for Commercial Buildings Using 1D Convolutional Neural Networks

Abraham Kaligambe, Goro Fujita

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

Many Commercial Buildings have employed smart meters to measure load consumption data at real-time intervals and then utilized by the Energy Management System (EMS). Load Forecasting based on historical load data is of key importance for effective operation, planning, and optimization of energy for Commercial Buildings. However, designing an accurate Load Forecasting Model is still an on-going challenge. Our methodology involved the usage of Deep Neural Networks (DNN) for Short-Term Load Forecasting. A special architecture of 1-Dimensional Convolutional Neural Networks (1D CNN) known as WaveNet was employed in our method because of its ability to extract rich features from historical load data sequences. A benchmark load consumption dataset of a Commercial Building for the fiscal year 2017 in Kyushu-Japan was used as a case study. Our model was evaluated and compared to other Machine Learning techniques for Forecasting. When tested on the same dataset, it outperformed them all.

本文言語English
ホスト出版物のタイトル2020 IEEE PES/IAS PowerAfrica, PowerAfrica 2020
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781728167466
DOI
出版ステータスPublished - 2020 8
イベント7th Annual IEEE PES/IAS PowerAfrica Conference, PowerAfrica 2020 - Virtual, Nairobi, Kenya
継続期間: 2020 8 252020 8 28

出版物シリーズ

名前2020 IEEE PES/IAS PowerAfrica, PowerAfrica 2020

Conference

Conference7th Annual IEEE PES/IAS PowerAfrica Conference, PowerAfrica 2020
国/地域Kenya
CityVirtual, Nairobi
Period20/8/2520/8/28

ASJC Scopus subject areas

  • 安全性、リスク、信頼性、品質管理
  • 地理、計画および開発
  • エネルギー工学および電力技術
  • 再生可能エネルギー、持続可能性、環境
  • 電子工学および電気工学

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