![]() ![]() ![]() Producer: Takero Hisamatsu, Tatsuro Hatanaka, Masahiko Ibaraki, Hiroaki Kitano, Osamu Kubota, Hiroyoshi Koiwai.Writer: Nobuhiro Watsuki (manga), Kiyomi Fujii, Keishi Ohtomo.PB - Institute of Electrical and Electronics Engineers Inc. T3 - 2020 IEEE PES/IAS PowerAfrica, PowerAfrica 2020īT - 2020 IEEE PES/IAS PowerAfrica, PowerAfrica 2020 When tested on the same dataset, it outperformed them all. Our model was evaluated and compared to other Machine Learning techniques for Forecasting. A benchmark load consumption dataset of a Commercial Building for the fiscal year 2017 in Kyushu-Japan was used as a case study. 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. Our methodology involved the usage of Deep Neural Networks (DNN) for Short-Term Load Forecasting. However, designing an accurate Load Forecasting Model is still an on-going challenge. Load Forecasting based on historical load data is of key importance for effective operation, planning, and optimization of energy for Commercial Buildings. When tested on the same dataset, it outperformed them all.ĪB - 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). N2 - 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). T1 - Short-Term Load Forecasting for Commercial Buildings Using 1D Convolutional Neural NetworksĬopyright 2020 Elsevier B.V., All rights reserved. When tested on the same dataset, it outperformed them all.", ![]() Abstract = "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). ![]()
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