(234b) Harnessing AI and Spatiotemporal Data for Improved Energy Systems Optimization and Electrification Strategies

Meeting the growing global energy demand sustainably, especially in regions with limited or no access to electricity, requires innovative approaches in energy system optimization and electricity supply chain planning. Traditional cost optimization and planning models often lack high-resolution spatiotemporal granularity, leading to suboptimal decision-making, inefficient resource allocation, and poor characterization of local energy demand dynamics [1]. To bridge these gaps, this study introduces a novel integrated modeling framework that synergistically combines advanced Artificial Intelligence (AI) techniques and comprehensive spatiotemporal data processing to significantly enhance the accuracy and robustness of energy system optimization and planning, further leading to well informed decision-making in electricity supply chain. The prevailing limitations of existing methodologies include insufficient spatial resolution in energy demand forecasts and inadequate temporal detail regarding energy consumption patterns and renewable generation variability [2]. These limitations result in critical mismatches between energy supply infrastructure pathways and actual localized demand, causing resource inefficiencies and higher economic costs. Furthermore, conventional models typically rely heavily on generalized assumptions and linear extrapolations that do not capture the complex interdependencies inherent in modern energy systems, particularly the spatially distributed nature of renewable energy resources and variability in regional energy demands [3].

To overcome these limitations, our proposed methodology leverages high-resolution spatial data, including detailed population distributions, current and proposed infrastructure mapping, renewable resource potential maps, and terrain and land-use characteristics. Temporal resolution is achieved through comprehensive historical and forecasted energy demand profiles, renewable generation data, and climate-weather patterns. By integrating these datasets, our approach employs state-of-the-art machine learning (ML) techniques, specifically gradient boosting method for predictive modeling of energy demand. These ML algorithms are rigorously trained and validated on high-resolution historical consumption data, socio-economic indicators, satellite imagery, and geospatial analytics to produce highly accurate and spatially explicit demand forecasts. The novel integration of deep-learning methods, specifically Convolutional Neural Networks (CNNs), further enhances renewable resource estimation by effectively processing large-scale satellite datasets. This precise spatial-temporal assessment of renewable potentials directly informs decision-making regarding optimal renewable energy project locations and necessary infrastructure expansions [4,5]. Moreover, advanced predictive analytics are utilized to estimate infrastructure development costs, considering location-specific variables such as accessibility, land characteristics, and proximity to existing infrastructure [6,7].

The novelty of this work lies in its iterative optimization structure, where high-resolution demand forecasts and renewable energy potentials derived through AI-driven models inform macro-level energy system optimization. The macro-level optimization outputs are subsequently refined spatially, ensuring electrification strategies align closely with on-the-ground reality. Through iterative feedback loops, the optimization model continuously updates and recalibrates predictions and recommendations, maintaining high accuracy and adaptability to dynamic conditions. Improved accuracy in electrification strategies enhances energy equity, ensuring infrastructure investments target genuinely underserved populations effectively and economically. Environmentally, better-informed renewable energy deployment mitigates negative impacts, avoiding unnecessary land usage and minimizing ecological disruption. Economically, the refined predictive capabilities optimize financial resource allocation, reducing investment risks and potentially lowering overall project costs. The broader societal benefit includes advancing Sustainable Development Goals (SDGs), notably SDG 7 (affordable and clean energy) and SDG 13 (climate action) [8].

In conclusion, this study provides a robust, innovative, and practically applicable approach for energy system optimization, harnessing the capabilities of AI and detailed spatiotemporal analytics to advance knowledge significantly within chemical and systems engineering domains. The model’s high-resolution spatiotemporal predictive capability effectively addresses existing gaps, ensuring better alignment between renewable energy integration, electrification strategies, and real-world energy demand dynamics. Ultimately, the proposed methodology advances the state-of-the-art in energy planning, providing industries, stakeholders, policymakers, and researchers with powerful tool for achieving sustainable and equitable global energy supply chain.

Literature cited:

1. Egli F, Agutu C, Steffen B, Schmidt TS. The cost of electrifying all households in 40 Sub-Saharan African countries by 2030. Nature communications. 2023 Aug 21;14(1):5066. https://doi.org/10.1038/s41467-023-40612-3
2. Forootan M., Larki I., Zahedi R., & Ahmadi A.. Machine learning and deep learning in energy systems: a review. Sustainability 2022;14(8):4832. https://doi.org/10.3390/su14084832
3. Tong D., Farnham D., Duan L., Zhang Q., Lewis N., Caldeira K.et al.. Geophysical constraints on the reliability of solar and wind power worldwide. Nature Communications 2021;12(1). https://doi.org/10.1038/s41467-021-26355-z
4. Malerba M. , Wright N. , & Macreadie P.. A continental-scale assessment of density, size, distribution and historical trends of farm dams using deep learning convolutional neural networks. Remote Sensing 2021;13(2):319. https://doi.org/10.3390/rs13020319
5. Nastasi B, Majidi Nezhad M. GIS and remote sensing for renewable energy assessment and maps. Energies. 2021 Dec 21;15(1):14. https://doi.org/10.3390/en15010014
6. Oakleaf J., Kennedy C., Baruch‐Mordo S., Gerber J., West P., Johnson J.et al.. Mapping global development potential for renewable energy, fossil fuels, mining and agriculture sectors. Scientific Data 2019;6(1). https://doi.org/10.1038/s41597-019-0084-8
7. Li G. , Luo T. , Liu R. , Song C. , Zhao C. , Wu S. et al.. Integration of carbon dioxide removal (CDR) technology and artificial intelligence (AI) in energy system optimization. Processes 2024;12(2):402. https://doi.org/10.3390/pr12020402
8. United Nations. Transforming our world: the 2030 agenda for sustainable development. New York: United Nations; 2015. https://sdgs.un.org/2030agenda