[HTML][HTML] Artificial intelligence based anomaly detection of energy consumption in buildings: A review, current trends and new perspectives

Y Himeur, K Ghanem, A Alsalemi, F Bensaali, A Amira - Applied Energy, 2021 - Elsevier
Enormous amounts of data are being produced everyday by sub-meters and smart sensors
installed in residential buildings. If leveraged properly, that data could assist end-users …

[HTML][HTML] Customer engagement strategies in retail electricity markets: A comprehensive and comparative review

H Hampton, A Foley, DF Del Rio, B Smyth… - Energy Research & …, 2022 - Elsevier
Retail electricity markets require development to ensure efficient and equitable pass through
of wholesale electricity costs to customers. Customer engagement has been heralded as a …

Stacking: A novel data-driven ensemble machine learning strategy for prediction and mapping of Pb-Zn prospectivity in Varcheh district, west Iran

M Hajihosseinlou, A Maghsoudi… - Expert Systems with …, 2024 - Elsevier
Various ensemble machine learning techniques have been widely studied and implemented
to construct the predictive models in different sciences, including bagging, boosting, and …

Exploring household emission patterns and driving factors in Japan using machine learning methods

P Chen, Y Wu, H Zhong, Y Long, J Meng - Applied Energy, 2022 - Elsevier
Given by the ambitious GHG mitigation targets set by governments worldwide, household is
playing an increasingly important role for reaching listed reduction goals. Consequently, a …

[HTML][HTML] A survey on artificial intelligence for reducing the climate footprint in healthcare

KP Das, J Chandra - Energy Nexus, 2023 - Elsevier
The primary mission of the healthcare sector is to protect from various ailments with
improved healthcare services and to use advanced diagnostic solutions to promote reliable …

[HTML][HTML] Development of MCS based-ensemble models using CEEMDAN decomposition and machine intelligence

S Garai, RK Paul - Intelligent Systems with Applications, 2023 - Elsevier
In this paper, stock price data has been predicted using several state-of-the-art
methodologies such as stochastic models, machine learning techniqus, and deep learning …

Systematic literature review on visual analytics of predictive maintenance in the manufacturing industry

X Cheng, JK Chaw, KM Goh, TT Ting, S Sahrani… - Sensors, 2022 - mdpi.com
The widespread adoption of cyber-physical systems and other cutting-edge digital
technology in manufacturing industry production facilities may motivate stakeholders to …

Artificial intelligence, household financial fragility and energy resources consumption: Impacts of digital disruption from a demand-based perspective

C Li, Y Zhang, X Li, Y Hao - Resources Policy, 2024 - Elsevier
Ensuring access to affordable energy for all is laid out among the 17 Sustainable
Development Goals and it remains an important open question as to how the popularity and …

Features and drivers of China's urban-rural household electricity consumption: Evidence from residential survey

D Wu, Y Geng, Y Zhang, W Wei - Journal of Cleaner Production, 2022 - Elsevier
China's rapid urbanization has resulted in increasing household electricity consumption
(HEC). However, significant urban-rural disparity exists due to different consumption …

AI for social good: AI and big data approaches for environmental decision-making

VOK Li, JCK Lam, J Cui - Environmental Science & Policy, 2021 - Elsevier
AI and big data technologies have been increasingly deployed to process complex,
heterogeneous, high-resolution environmental data, and generate results at greater speeds …