TY - JOUR
T1 - Industrial artificial intelligence based energy management system: Integrated framework for electricity load forecasting and fault prediction
AU - Hu, Yusha
AU - Li, Jigeng
AU - Hong, Mengna
AU - Ren, Jingzheng
AU - Man, Yi
N1 - Funding Information:
The work described in this paper was supported by the grant “Postdoctoral Fellowships Scheme” from the Research Committee of The Hong Kong Polytechnic University under project ID- P0031541 (account- G-YW4Y) and this work was also supported by Joint Supervision Scheme with the Chinese Mainland, Taiwan and Macao Universities - Other Chinese Mainland, Taiwan and Macao Universities under project ID- P0035156 (account- G-SB3R).
Publisher Copyright:
© 2022 Elsevier Ltd
PY - 2022/4/1
Y1 - 2022/4/1
N2 - Forecasting accuracy electricity load can help industrial enterprises optimise production scheduling based on peak and off-peak electricity prices. The electricity load forecasting results can be provided to an electricity system to improve electricity generation efficiency and minimize energy consumption by developing electricity generation plans in advance and by avoiding over or under the generation of electricity. However, because of the different informatization levels in different industries, few reliable intelligent electricity management systems are applied on the power supply side. Based on industrial big data and machine learning algorithms, this study proposes an integrated model to forecast short-term electricity load. The hybrid model based on the hybrid mode decomposition algorithms is proposed to decompose the total electricity load signal. To improve the generalisation ability of the forecasting model, a dynamic forecasting model is proposed based on the improved hybrid intelligent algorithm to forecast the short-term electricity load. The results show that the accuracy of the proposed dynamic integrated electricity load forecasting model is as high as 99%. The integrated framework could forecast abnormal electricity consumption in time and provide reliable evidence for production process scheduling.
AB - Forecasting accuracy electricity load can help industrial enterprises optimise production scheduling based on peak and off-peak electricity prices. The electricity load forecasting results can be provided to an electricity system to improve electricity generation efficiency and minimize energy consumption by developing electricity generation plans in advance and by avoiding over or under the generation of electricity. However, because of the different informatization levels in different industries, few reliable intelligent electricity management systems are applied on the power supply side. Based on industrial big data and machine learning algorithms, this study proposes an integrated model to forecast short-term electricity load. The hybrid model based on the hybrid mode decomposition algorithms is proposed to decompose the total electricity load signal. To improve the generalisation ability of the forecasting model, a dynamic forecasting model is proposed based on the improved hybrid intelligent algorithm to forecast the short-term electricity load. The results show that the accuracy of the proposed dynamic integrated electricity load forecasting model is as high as 99%. The integrated framework could forecast abnormal electricity consumption in time and provide reliable evidence for production process scheduling.
KW - Electricity load
KW - Dynamic forecasting model
KW - Energy system analysis
KW - Energy system optimisation
KW - Artificial intelligence
UR - https://www.sciencedirect.com/science/article/pii/S0360544222000986?dgcid=coauthor
UR - http://www.scopus.com/inward/record.url?scp=85122987401&partnerID=8YFLogxK
U2 - 10.1016/j.energy.2022.123195
DO - 10.1016/j.energy.2022.123195
M3 - Journal article
SN - 0360-5442
VL - 244
JO - Energy
JF - Energy
IS - Part B
M1 - 123195
ER -