A comprehensive review on deep learning approaches for short-term load forecasting

Y Eren, İ Küçükdemiral - Renewable and Sustainable Energy Reviews, 2024 - Elsevier
The balance between supplied and demanded power is a crucial issue in the economic
dispatching of electricity energy. With the emergence of renewable sources and data-driven …

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 …

[HTML][HTML] 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 …

Regression model-based short-term load forecasting for university campus load

M Madhukumar, A Sebastian, X Liang, M Jamil… - IEEE …, 2022 - ieeexplore.ieee.org
Load forecasting is a critical aspect for power systems planning, operation and control. In
this paper, as part of research efforts of an ambitious project at Memorial University of …

A CNN and LSTM-based multi-task learning architecture for short and medium-term electricity load forecasting

S Zhang, R Chen, J Cao, J Tan - Electric power systems research, 2023 - Elsevier
Electricity load forecasting is the forecast of power load in the future period based on
historical load and its related factors. It is of great importance for power system planning …

Reinforcement learning in sustainable energy and electric systems: A survey

T Yang, L Zhao, W Li, AY Zomaya - Annual Reviews in Control, 2020 - Elsevier
The dynamic nature of sustainable energy and electric systems can vary significantly along
with the environment and load change, and they represent the features of multivariate, high …

[HTML][HTML] Deep learning for intelligent demand response and smart grids: A comprehensive survey

P Boopathy, M Liyanage, N Deepa, M Velavali… - Computer science …, 2024 - Elsevier
Electricity is one of the mandatory commodities for mankind today. To address challenges
and issues in the transmission of electricity through the traditional grid, the concepts of smart …

A novel method for sentiment classification of drug reviews using fusion of deep and machine learning techniques

ME Basiri, M Abdar, MA Cifci, S Nemati… - Knowledge-Based …, 2020 - Elsevier
Nowadays, the development of new computer-based technologies has led to rapid increase
in the volume of user-generated textual content on the website. Patient-written medical and …

A review on the integration of probabilistic solar forecasting in power systems

B Li, J Zhang - Solar Energy, 2020 - Elsevier
As one of the fastest growing renewable energy sources, the integration of solar power
poses great challenges to power systems due to its variable and uncertain nature. As an …

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 …