Carbon pricing prediction based on wavelet transform and K-ELM optimized by bat optimization algorithm in China ETS: the case of Shanghai and Hubei carbon markets

Tackling the Risk of Stranded Electricity Assets with Machine Learning and Artificial Intelligence

“…Tackling the Risk of Stranded Electricity Assets with Machine Learning and Artificial Intelligence DOI: http://dx.doi.org /10.5772/intechopen.93488 expected that as this shift continues, new opportunities for ML and AI applications will become available, including in modeling consumer behavior and facilitating sustainable behavior change energy consumption action [3, 65,118,119], estimating and predicting the marginal emissions of residential energy utilization and thermal comfort in buildings in real time, on a scale of hours [57,118], and game-theoretic modeling and design of socially beneficial energy policies like social norms, public opinions, stakeholder engagement, and education efforts [120][121][122]. Other breakthrough innovations might displace fossil fuels leading to stranding, and creating opportunities for ML-based electricity pricing techniques and rate design to set dynamic pricing of carbon, electricity, and consumer choice [1,[123][124][125][126][127], and multiobjective optimization to compute Pareto-optimal solutions for climate engineering, climate informatics, and solar geoengineering [58,[128][129][130]. There is a possibility that these technological innovations could create a sudden improvement in market evaluation of the renewable energy industries, while some assets of related carbonintensive industries become stranded due to obsolescence, write-offs, or retirements.…”

On the Morality of Artificial Intelligence [Commentary]

“…While changes in our climate can be abstract, quantified in degrees of warming or tons of CO2, climate change will also have very concrete impacts on society, for instance by decreasing crop yield, increasing the frequency of extreme weather events such as hurricanes and storms, and impacting biodiversity. There are a myriad of ways in which ML can help face these, whether it be by analyzing real-time images and recordings of ecosystems to detect species [Duhart et al, 2018] and deforestation [McDowell et al, 2015], improving disaster preparation and response by generating real-time maps from satellite imagery [Voigt et al, 2007] and even setting an optimal price on carbon to accelerate the transition to a low-carbon energy economy [Wei et al, 2018]. Finally, while we are far from being able to predict the exact impact that increasing the carbon tax will have on the different levels of society and industry (i.e.…”

An ensemble‐driven long short‐term memory model based on mode decomposition for carbon price forecasting of all eight carbon trading pilots in China

“…Model transformation methods used in existing studies have mainly included empirical mode decomposition (EMD), 3 variational mode decomposition (VMD), 34 ensemble empirical mode decomposition (EEMD), 35 and wavelet transform (WT). 36 However, these methods are not sufficiently advanced. For example, EMD possesses problems in modal aliasing; EEMD overcomes this issue but contains residual noise.…”

“…Thus, ensemble learning based on mode transformation methods was introduced to forecast the carbon price in this study. Model transformation methods used in existing studies have mainly included empirical mode decomposition (EMD), 3 variational mode decomposition (VMD), 34 ensemble empirical mode decomposition (EEMD), 35 and wavelet transform (WT) 36 . However, these methods are not sufficiently advanced.…”