Sustainable residential building energy consumption forecasting for smart cities using optimal weighted voting ensemble learning
Authors:
- Mofadal AlymaniMAMofadal Alymani
- Hanan Abdullah MengashHMHanan Abdullah Mengash
- Mohammed AljebreenMAMohammed Aljebreen
- Naif AlasmariNANaif Alasmari
- Randa AllafiRARanda Allafi
- Hussain AlshahraniHAHussain Alshahrani
- Mohamed Ahmed ElfakiMEMohamed Ahmed Elfaki
- Manar Ahmed HamzaMHManar Ahmed Hamza
- Amgad Atta AbdelmageedAAAmgad Atta Abdelmageed
- 18 May 2023
- journal article
- research article
- Published by Elsevier in Sustainable Energy Technologies and Assessments
- Vol. 57
Abstract
No abstract availableKeywords
Funding Information
- King Saud University
- Princess Nourah Bint Abdulrahman University (RSP2023R459)
- King Khalid University
- Deanship of Scientific Research, King Khalid University (RGP2/206/44)
This publication has 21 references indexed in Scilit:
- A smart healthcare monitoring system for heart disease prediction based on ensemble deep learning and feature fusionInformation Fusion, 2020
- Bi-LSTM Model to Increase Accuracy in Text Classification: Combining Word2vec CNN and Attention MechanismApplied Sciences, 2020
- Multiple Electric Energy Consumption Forecasting Using a Cluster-Based Strategy for Transfer Learning in Smart BuildingSensors, 2020
- Transfer learning with deep neural networks for model predictive control of HVAC and natural ventilation in smart buildingsJournal of Cleaner Production, 2020
- A comparison of machine learning algorithms for forecasting indoor temperature in smart buildingsEnergy Systems, 2020
- A platform for power management based on indoor localization in smart buildings using long short‐term neural networksTransactions on Emerging Telecommunications Technologies, 2020
- An Enhanced Version of Black Hole Algorithm via Levy Flight for Optimization and Data Clustering ProblemsIEEE Access, 2019
- A methodology for energy multivariate time series forecasting in smart buildings based on feature selectionEnergy and Buildings, 2019
- A Comparative Study of Time Series Forecasting Methods for Short Term Electric Energy Consumption Prediction in Smart BuildingsEnergies, 2019
- Machine Learning for Smart Building ApplicationsACM Computing Surveys, 2019