Deep Learning and Artificial Intelligence in Sustainability: A Review of SDGs, Renewable Energy, and Environmental Health

Abstract

Artificial intelligence (AI) and deep learning (DL) have shown tremendous potential in driving sustainability across various sectors. This paper reviews recent advancements in AI and DL and explores their applications in achieving sustainable development goals (SDGs), renewable energy, environmental health, and smart building energy management. AI has the potential to contribute to 134 of the 169 targets across all SDGs, but the rapid development of these technologies necessitates comprehensive regulatory oversight to ensure transparency, safety, and ethical standards. In the renewable energy sector, AI and DL have been effectively utilized in optimizing energy management, fault detection, and power grid stability. They have also demonstrated promise in enhancing waste management and predictive analysis in photovoltaic power plants. In the field of environmental health, the integration of AI and DL has facilitated the analysis of complex spatial data, improving exposure modeling and disease prediction. However, challenges such as the explainability and transparency of AI and DL models, the scalability and high dimensionality of data, the integration with next-generation wireless networks, and ethics and privacy concerns need to be addressed. Future research should focus on enhancing the explainability and transparency of AI and DL models, developing scalable algorithms for processing large datasets, exploring the integration of AI with next-generation wireless networks, and addressing ethical and privacy considerations. Additionally, improving the energy efficiency of AI and DL models is crucial to ensure the sustainable use of these technologies. By addressing these challenges and fostering responsible and innovative use, AI and DL can significantly contribute to a more sustainable future.

Empowering Sustainability: A Consumer-Centric Analysis Based on Advanced Electricity Consumption Predictions

“…If seasonal patterns are needed in the time series, the seasonal term is added to ARIMA and becomes the seasonal ARIMA model (SARIMA). The model can be written as Equation ( 5) [63].…”

“…smoothing_level determines the smoothing coefficient of the error component, smoothing_trend determines the trend component's smoothing coefficient, and smoothing_seasonal determines the smoothing coefficient of the seasonal component. These coefficients affect how "close" or "smooth" the model will be to the data [62,63,[65][66][67].…”

The leading global health challenges in the artificial intelligence era

“…In waste management, AI reduces fuel consumption and emissions, increases recycling rates, and reduces landfill waste ( 143 ). GeoAI is one of the emerging AI tools that can handle complex spatial and temporal data to adjust algorithms and workflows according to the specific characteristics of spatial processes ( 136 , 144 ). It can develop various environmental exposure models across different geographical regions in prospective and retrospective approaches ( 136 ).…”

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“…DL algorithms are ANNs with a deep architecture that are capable of processing enormous amounts of data; they have outperformed state-of-the-art results in a variety of classification and regression tasks [25][26][27]. DL algorithms are expected to overcome the challenges posed by large-scale data [28]. Moreover, they can learn and discriminate features in a hierarchical way [19].…”

“…Moreover, they can learn and discriminate features in a hierarchical way [19]. DL approaches have been applied to various domains of renewable energy and have provided higher forecast accuracy for wind and other renewable energies than traditional methods [19,28]. Recent studies have demonstrated the use of DL techniques and good ML tools for complex pattern detection, regression analysis, and forecasting [29].…”