What question did this study set out to answer?

Develop an integrated framework combining artificial intelligence load forecasting and hybrid metaheuristic optimization to guide multi-objective capacity expansion planning in decarbonizing power grids.

August 20, 2026Energy ReportsOpen Access

Integration of AI-based load forecasting and hybrid metaheuristic optimization for sustainable capacity expansion planning in decarbonized solar energy systems

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Authors

SSS SivakumarCarl Zeiss (Germany)KRK. RajeshKalasalingam Academy of Research and Education PMPALLIKONDA RAJASEKARAN MKalasalingam Academy of Research and Education

Discussion

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Implication

Computational modeling demonstrates reduced costs and improved grid reliability in decarbonized power systems, indicating enhanced feasibility for long-term solar capacity expansion.

Key Points

Develop an integrated framework combining artificial intelligence load forecasting and hybrid metaheuristic optimization to guide multi-objective capacity expansion planning in decarbonizing power grids.
Coupled a Grasshopper Optimization Algorithm with Random Forest (GOA-RF) to forecast electrical load demand.
Formulated a multi-objective capacity expansion planning (MO-CEP) model and solved it using a novel hybrid Differential Evolution-based Harris Hawks Optimization (DEHHO) algorithm over 12-year and 28-year horizons.
The GOA-RF demand forecasting model achieved an R² of 99.81% for training and 98.76% for testing, with an MAE of 0.1079 and an RMSE of 0.1573.
Transitioning from the baseline to a high solar scenario reduced overall system costs by 8.12% over a 12-year horizon and by 16.7% over a 28-year horizon.
Loss of load probability (LOLP) and unmet electricity demand improved by 86.1% over 12 years and by 79.5% over 28 years.

Cite This Study

Sivakumar et al. (2026) studied this question.

synapsesocial.com/papers/6a86b4cb8a91293e6a1cc47b https://doi.org/10.1016/j.egyr.2026.109622

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August 20, 2026Energy ReportsOpen Access

Integration of AI-based load forecasting and hybrid metaheuristic optimization for sustainable capacity expansion planning in decarbonized solar energy systems

View Full Paper

Ask AI

Bookmark

Authors

SSS SivakumarCarl Zeiss (Germany)KRK. RajeshKalasalingam Academy of Research and Education PMPALLIKONDA RAJASEKARAN MKalasalingam Academy of Research and Education

Discussion

Member takes

Implication

Computational modeling demonstrates reduced costs and improved grid reliability in decarbonized power systems, indicating enhanced feasibility for long-term solar capacity expansion.

Key Points

Develop an integrated framework combining artificial intelligence load forecasting and hybrid metaheuristic optimization to guide multi-objective capacity expansion planning in decarbonizing power grids.
Coupled a Grasshopper Optimization Algorithm with Random Forest (GOA-RF) to forecast electrical load demand.
Formulated a multi-objective capacity expansion planning (MO-CEP) model and solved it using a novel hybrid Differential Evolution-based Harris Hawks Optimization (DEHHO) algorithm over 12-year and 28-year horizons.
The GOA-RF demand forecasting model achieved an R² of 99.81% for training and 98.76% for testing, with an MAE of 0.1079 and an RMSE of 0.1573.
Transitioning from the baseline to a high solar scenario reduced overall system costs by 8.12% over a 12-year horizon and by 16.7% over a 28-year horizon.
Loss of load probability (LOLP) and unmet electricity demand improved by 86.1% over 12 years and by 79.5% over 28 years.

Cite This Study

Sivakumar et al. (2026) studied this question.

synapsesocial.com/papers/6a86b4cb8a91293e6a1cc47b https://doi.org/10.1016/j.egyr.2026.109622

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