Energy forecasting: A review and outlook

T Hong, P Pinson, Y Wang, R Weron… - IEEE Open Access …, 2020 - ieeexplore.ieee.org
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 …

Forecast combinations: An over 50-year review

X Wang, RJ Hyndman, F Li, Y Kang - International Journal of Forecasting, 2023 - Elsevier
Forecast combinations have flourished remarkably in the forecasting community and, in
recent years, have become part of mainstream forecasting research and activities …

Review of smart meter data analytics: Applications, methodologies, and challenges

Y Wang, Q Chen, T Hong… - IEEE Transactions on …, 2018 - ieeexplore.ieee.org
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 …

Review of family-level short-term load forecasting and its application in household energy management system

P Ma, S Cui, M Chen, S Zhou, K Wang - Energies, 2023 - mdpi.com
With the rapid development of smart grids and distributed energy sources, the home energy
management system (HEMS) is becoming a hot topic of research as a hub for connecting …

Load forecasting techniques for power system: Research challenges and survey

N Ahmad, Y Ghadi, M Adnan, M Ali - IEEE Access, 2022 - ieeexplore.ieee.org
The main and pivot part of electric companies is the load forecasting. Decision-makers and
think tank of power sectors should forecast the future need of electricity with large accuracy …

Using Bayesian deep learning to capture uncertainty for residential net load forecasting

M Sun, T Zhang, Y Wang, G Strbac… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
Decarbonization of electricity systems drives significant and continued investments in
distributed energy sources to support the cost-effective transition to low-carbon energy …

Review of low voltage load forecasting: Methods, applications, and recommendations

S Haben, S Arora, G Giasemidis, M Voss, DV Greetham - Applied Energy, 2021 - Elsevier
The increased digitalisation and monitoring of the energy system opens up numerous
opportunities to decarbonise the energy system. Applications on low voltage, local networks …

An improved quantile regression neural network for probabilistic load forecasting

W Zhang, H Quan, D Srinivasan - IEEE Transactions on Smart …, 2018 - ieeexplore.ieee.org
Accurate and reliable load forecasting is essential for decision-making processes in the
electric power industry. As the power industry transitions toward decarbonization, distributed …

An ensemble methodology for hierarchical probabilistic electric vehicle load forecasting at regular charging stations

L Buzna, P De Falco, G Ferruzzi, S Khormali, D Proto… - Applied Energy, 2021 - Elsevier
Transportation electrification is a valid option for supporting decarbonization efforts but, at
the same time, the growing number of electric vehicles will produce new and unpredictable …

Impact of high renewable penetration on the power system operation mode: A data-driven approach

Q Hou, E Du, N Zhang, C Kang - IEEE Transactions on Power …, 2019 - ieeexplore.ieee.org
The high penetration of renewable energy will substantially change the power system
operation. Traditionally, the annual operation of a power system can be represented by …