Energy forecasting: A review and outlook
Forecasting has been an essential part of the power and energy industry. Researchers and
practitioners have contributed thousands of papers on forecasting electricity demand and …
practitioners have contributed thousands of papers on forecasting electricity demand and …
Load forecasting models in smart grid using smart meter information: a review
F Dewangan, AY Abdelaziz, M Biswal - Energies, 2023 - mdpi.com
The smart grid concept is introduced to accelerate the operational efficiency and enhance
the reliability and sustainability of power supply by operating in self-control mode to find and …
the reliability and sustainability of power supply by operating in self-control mode to find and …
Review of smart meter data analytics: Applications, methodologies, and challenges
The widespread popularity of smart meters enables an immense amount of fine-grained
electricity consumption data to be collected. Meanwhile, the deregulation of the power …
electricity consumption data to be collected. Meanwhile, the deregulation of the power …
A hybrid deep learning model for short-term PV power forecasting
P Li, K Zhou, X Lu, S Yang - Applied Energy, 2020 - Elsevier
The integration of PV power brings great economic and environmental benefits. However,
the high penetration of PV power may challenge the planning and operation of the existing …
the high penetration of PV power may challenge the planning and operation of the existing …
基于人工智能技术的新型电力系统负荷预测研究综述
韩富佳， 王晓辉， 乔骥， 史梦洁， 蒲天骄 - 中国电机工程学报, 2023 - epjournal.csee.org.cn
在“双碳” 目标的驱动下, 构建以新能源为主体的新型电力系统是促进现代电力系统低碳转型发展
的重要前提与必然趋势. 由于复杂易变的多元负荷是新型电力系统的重要组成部分 …
的重要前提与必然趋势. 由于复杂易变的多元负荷是新型电力系统的重要组成部分 …
An efficient deep learning framework for intelligent energy management in IoT networks
T Han, K Muhammad, T Hussain… - IEEE Internet of …, 2020 - ieeexplore.ieee.org
Green energy management is an economical solution for better energy usage, but the
employed literature lacks focusing on the potentials of edge intelligence in controllable …
employed literature lacks focusing on the potentials of edge intelligence in controllable …
Using Bayesian deep learning to capture uncertainty for residential net load forecasting
Decarbonization of electricity systems drives significant and continued investments in
distributed energy sources to support the cost-effective transition to low-carbon energy …
distributed energy sources to support the cost-effective transition to low-carbon energy …
Multi-temporal-spatial-scale temporal convolution network for short-term load forecasting of power systems
L Yin, J Xie - Applied Energy, 2021 - Elsevier
With the advancement of power market reform, accurate load forecasting can ensure the
stable operation of power systems increasingly. The randomness of feature change such as …
stable operation of power systems increasingly. The randomness of feature change such as …
Review of low voltage load forecasting: Methods, applications, and recommendations
The increased digitalisation and monitoring of the energy system opens up numerous
opportunities to decarbonise the energy system. Applications on low voltage, local networks …
opportunities to decarbonise the energy system. Applications on low voltage, local networks …
Combining probabilistic load forecasts
Probabilistic load forecasts provide comprehensive information about future load
uncertainties. In recent years, many methodologies and techniques have been proposed for …
uncertainties. In recent years, many methodologies and techniques have been proposed for …