A Review for Green Energy Machine Learning and AI Services

Abstract

There is a growing demand for Green AI (Artificial Intelligence) technologies in the market and society, as it emerges as a promising technology. Green AI technologies are used to create sustainable solutions and reduce the environmental impact of AI. This paper focuses on describing the services of Green AI and the challenges associated with it at the community level. This article also highlights the accuracy levels of machine learning algorithms for various time periods. The process of choosing the appropriate input parameters for weather, locations, and complexity is outlined in this paper to examine the ML algorithms. For correcting the algorithm performance parameters, metrics like RMSE (root mean square error), MSE (mean square error), MAE (mean absolute error), and MPE (mean percentage error) are considered. Considering the performance and results of this review, the LSTM (long short-term memory) performed well in most cases. This paper concludes that highly advanced techniques have dramatically improved forecasting accuracy. Finally, some guidelines are added for further studies, needs, and challenges. However, there is still a need for more solutions to the challenges, mainly in the area of electricity storage.

Integrating AI-Driven Green Finance Strategies for Sustainable Development: A Comparative Analysis of Renewable Energy Investments in Germany and Denmark

“…Achieving the Sustainable Development Goals requires a combination of the green economy, technology, and other resources. Since AI-driven, processes have the potential to dramatically change environmental perceptions and influence economic decision-making; these processes should be integrated into a green economy (Mehta et al, 2023).…”

Time-Series Power Forecasting for Wind and Solar Energy Based on the SL-Transformer

“…Traditionally, power forecasting of green energy has been accomplished using statistical time-series models such as ARIMA, SARIMA, and exponential smoothing methods. These methods rely on historical power data to capture the seasonal and temporal trends in power demand, making them suitable for short-term forecasting [16,17]. However, they fail to capture the complex and nonlinear relationships between the various factors affecting power demand, such as weather patterns and power supply fluctuations.…”

Improving Automated Machine-Learning Systems through Green AI

“…Instead, both factors should be considered jointly as research continues to explore the intersection of efficiency and environmental impact [17,18]. A burgeoning solution to the challenge of energy-intensive algorithms is green or sustainable AI [19][20][21][22], which underscores the growing pursuit of ecologically mindful practices within the realm of AI and AutoML. Key strategies include precision-energy tradeoffs, energy-aware neural architecture search, model compression, and multi-objective Pareto optimization considering accuracy, latency, and power [23].…”