TY - JOUR
T1 - Smart and intelligent energy monitoring systems
T2 - A comprehensive literature survey and future research guidelines
AU - Hussain, Tanveer
AU - Min Ullah, Fath U.
AU - Muhammad, Khan
AU - Rho, Seungmin
AU - Ullah, Amin
AU - Hwang, Eenjun
AU - Moon, Jihoon
AU - Baik, Sung Wook
N1 - Funding Information:
National Research Foundation of Korea Funding information
Funding Information:
This work was supported by the National Research Foundation of Korea (NRF), grant funded by the Korea government (MSIT) (No. 2019M3F2A1073179).
PY - 2021
Y1 - 2021
N2 - Computationally intelligent energy forecasting methods for appropriate energy management at the consumer/producer side have a positive impact on the preservation of energy and play a constructive role in tackling global climate change. The energy production and consumption are very high worldwide, demanding intelligent methods with real-world implementation potentials for appropriate energy management. In this paper, we survey the existing intelligent load forecasting (ILF) systems, highlight their advantages and downsides, and briefly discuss the workflow of the employed literature. Furthermore, we debate on the existing load forecasting datasets and their features along with a brief overview of the challenges confronted by researchers using these datasets. Distinct from previous survey papers, we provide a detailed review of performance evaluation metrics and comparison of employed methods for energy load forecasting, thereby concluding the need of efficient, effective, and adoptable ILF methods functional in real-world scenarios. Finally, we assess the employed techniques and deliver future research opportunities based on the derived conclusions from existing research works. This paper delivers the overall energy forecasting literature in a compact form with possible future insights for researchers working in ILF domain.
AB - Computationally intelligent energy forecasting methods for appropriate energy management at the consumer/producer side have a positive impact on the preservation of energy and play a constructive role in tackling global climate change. The energy production and consumption are very high worldwide, demanding intelligent methods with real-world implementation potentials for appropriate energy management. In this paper, we survey the existing intelligent load forecasting (ILF) systems, highlight their advantages and downsides, and briefly discuss the workflow of the employed literature. Furthermore, we debate on the existing load forecasting datasets and their features along with a brief overview of the challenges confronted by researchers using these datasets. Distinct from previous survey papers, we provide a detailed review of performance evaluation metrics and comparison of employed methods for energy load forecasting, thereby concluding the need of efficient, effective, and adoptable ILF methods functional in real-world scenarios. Finally, we assess the employed techniques and deliver future research opportunities based on the derived conclusions from existing research works. This paper delivers the overall energy forecasting literature in a compact form with possible future insights for researchers working in ILF domain.
KW - energy consumption modeling
KW - energy management
KW - energy monitoring
KW - energy survey
KW - intelligent load forecasting
KW - smart energy systems
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U2 - 10.1002/er.6093
DO - 10.1002/er.6093
M3 - Review article
AN - SCOPUS:85099227010
JO - International Journal of Energy Research
JF - International Journal of Energy Research
SN - 0363-907X
ER -