A systematic review of Green AI
With the ever‐growing adoption of artificial intelligence (AI)‐based systems, the carbon
footprint of AI is no longer negligible. AI researchers and practitioners are therefore urged to …
footprint of AI is no longer negligible. AI researchers and practitioners are therefore urged to …
The AI gambit: leveraging artificial intelligence to combat climate change—opportunities, challenges, and recommendations
In this article, we analyse the role that artificial intelligence (AI) could play, and is playing, to
combat global climate change. We identify two crucial opportunities that AI offers in this …
combat global climate change. We identify two crucial opportunities that AI offers in this …
Building energy consumption prediction for residential buildings using deep learning and other machine learning techniques
The high proportion of energy consumed in buildings has engendered the manifestation of
many environmental problems which deploy adverse impacts on the existence of mankind …
many environmental problems which deploy adverse impacts on the existence of mankind …
Artificial intelligence, systemic risks, and sustainability
Automated decision making and predictive analytics through artificial intelligence, in
combination with rapid progress in technologies such as sensor technology and robotics are …
combination with rapid progress in technologies such as sensor technology and robotics are …
The renewable energy role in the global energy Transformations
In a comprehensive analysis of the global transition towards renewable energy, the study
revealed significant disparities in adoption rates and technological advancements across …
revealed significant disparities in adoption rates and technological advancements across …
[PDF][PDF] The computational limits of deep learning
Deep learning's recent history has been one of achievement: from triumphing over humans
in the game of Go to world-leading performance in image classification, voice recognition …
in the game of Go to world-leading performance in image classification, voice recognition …
Lead federated neuromorphic learning for wireless edge artificial intelligence
In order to realize the full potential of wireless edge artificial intelligence (AI), very large and
diverse datasets will often be required for energy-demanding model training on resource …
diverse datasets will often be required for energy-demanding model training on resource …
Trustworthy ai: A computational perspective
In the past few decades, artificial intelligence (AI) technology has experienced swift
developments, changing everyone's daily life and profoundly altering the course of human …
developments, changing everyone's daily life and profoundly altering the course of human …
Methods of forecasting electric energy consumption: A literature review
RV Klyuev, ID Morgoev, AD Morgoeva, OA Gavrina… - Energies, 2022 - mdpi.com
Balancing the production and consumption of electricity is an urgent task. Its implementation
largely depends on the means and methods of planning electricity production. Forecasting is …
largely depends on the means and methods of planning electricity production. Forecasting is …
Machine learning techniques applied to mechanical fault diagnosis and fault prognosis in the context of real industrial manufacturing use-cases: a systematic literature …
M Fernandes, JM Corchado, G Marreiros - Applied Intelligence, 2022 - Springer
When put into practice in the real world, predictive maintenance presents a set of challenges
for fault detection and prognosis that are often overlooked in studies validated with data from …
for fault detection and prognosis that are often overlooked in studies validated with data from …