On the benefits of using metaheuristics in the hyperparameter tuning of deep learning models for energy load forecasting

N Bacanin, C Stoean, M Zivkovic, M Rakic… - Energies, 2023 - mdpi.com
An effective energy oversight represents a major concern throughout the world, and the
problem has become even more stringent recently. The prediction of energy load and …

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 …

Residential energy consumption forecasting using deep learning models

PVB Ramos, SM Villela, WN Silva, BH Dias - Applied Energy, 2023 - Elsevier
The energy sector plays an important role in socioeconomic and environmental
development. Accurately forecasting energy demand across various time horizons can yield …

A novel two-stage seasonal grey model for residential electricity consumption forecasting

P Du, S Sun, S Wang, J Wu - Energy, 2022 - Elsevier
Accurate electricity consumption forecasting plays a significant role in power production and
supply and power dispatching. Thus, a new hybrid model combing a grey model with …

Uncertainty management in electricity demand forecasting with machine learning and ensemble learning: case studies of COVID-19 in the US metropolitans

MR Baker, KH Jihad, H Al-Bayaty, A Ghareeb… - … Applications of Artificial …, 2023 - Elsevier
Improving load forecasting is becoming increasingly crucial for power system management
and operational research. Disruptive influences can seriously impact both the supply and …

Pulse-diagnosis-inspired multi-feature extraction deep network for short-term electricity load forecasting

H Wu, Y Liang, J Heng - Applied Energy, 2023 - Elsevier
Forecasting short-term electricity load (STEL) is a very important but challenging task by the
fact that the series dynamic change involves in multiple patterns, such as long short-term …

Which industrial sectors are affected by artificial intelligence? A bibliometric analysis of trends and Perspectives

L Espina-Romero, JG Noroño Sánchez… - Sustainability, 2023 - mdpi.com
In recent times, artificial intelligence (AI) has been generating a significant impact in various
industry sectors, which implies that companies must be ready to adjust to this promising start …

Energy consumption optimization of building air conditioning system via combining the parallel temporal convolutional neural network and adaptive opposition …

H Sun, Y Niu, C Li, C Zhou, W Zhai, Z Chen, H Wu… - Energy, 2022 - Elsevier
Heating, ventilation, and air-conditioning systems provide a comfortable indoor thermal
environment, but high energy consumption is often necessary to achieve an adequate level …

A Bayesian optimization-based LSTM model for wind power forecasting in the Adama district, Ethiopia

ET Habtemariam, K Kekeba, M Martínez-Ballesteros… - Energies, 2023 - mdpi.com
Renewable energies, such as solar and wind power, have become promising sources of
energy to address the increase in greenhouse gases caused by the use of fossil fuels and to …

[HTML][HTML] Electricity demand forecasting based on feature extraction and optimized backpropagation neural network

EON Jnr, YY Ziggah - e-Prime-Advances in Electrical Engineering …, 2023 - Elsevier
As the global population is growing at a high rate, so is the electricity demand also
increasing at a faster rate. This exerts pressure on electricity-generating plants and …