Optimizing business strategies for energy through machine learning involves using predictive analytics for accurate energy demand and price forecasting, enhancing operational efficiency through resource optimization and predictive maintenance, and optimizing renewable energy integration into the energy grid. This approach maximizes production, reduces costs, and ensures stability in energy supply. The novelty of integrating deep reinforcement learning (DRL) in energy management lies in its ability to adapt and optimize operational strategies in real-time, autonomously leveraging advanced machine learning techniques to handle dynamic and complex energy environments. The study’s outcomes demonstrate the effectiveness of DRL in optimizing energy management strategies. Statistical validity tests revealed shallow error values [MAE: 1.056 × 10(− 13) and RMSE: 1.253 × 10(− 13)], indicating strong predictive accuracy and model robustness. Sensitivity analysis showed that heating and cooling energy consumption variations significantly impact total energy consumption, with predicted changes ranging from 734.66 to 835.46 units. Monte Carlo simulations revealed a mean total energy consumption of 850 units with a standard deviation of 50 units, underscoring the model’s robustness under various stochastic scenarios. Another significant result of the economic impact analysis was the comparison of different operational strategies. The analysis indicated that scenario 1 (high operational costs) and scenario 2 (lower operational costs) both resulted in profits of $70,000, despite differences in operational costs and revenues. However, scenario 3 (optimized strategy) demonstrated superior financial performance with a profit of $78,500. This highlights the importance of strategic operational improvements and suggests that efficiency optimization can significantly enhance profitability. In addition, the DRL-enhanced strategies showed a marked improvement in forecasting and managing demand fluctuations, leading to better resource allocation and reduced energy wastage. Integrating DRL improves operational efficiency and supports long-term financial viability, positioning energy systems for a more sustainable future.

이산화탄소는 기후변화를 야기시키는 주요 온실가스이다. 본 연구는 춘천시, 강릉시, 서울시 강남구 및 중랑구를 대상으로 토지이용유형별 식생관리에 기인한 에너지소비 및 탄소방출을 잔디깎기, 전정, 관수, 시비, 살충제시용 등의 식생관리실태를 면담설문 및 일부 실측을 통해 파악하였다. 동일 토지이용유형 내 식생관리강도는 대체로 도시간 및 구간 통계적으로 유의한 차이가 없었다. 수목관리에 의한 연간 총탄소방출량은 토지이용유형에 따라 단위피도면적당 36.0~209.7g/m2로서 교통용지에서 가장 많았다. 잔디관리에 의한 연간 총탄소방출량은 단위잔디면적당 7.4~69.3g/m2로서 공원에서 가장 많았다. 이들 총탄소방출량 중 수목의 경우 전정이 토지이용유형에 따라 96.8~99.7%를, 잔디의 경우 잔디깎기가 91.9~100%를 각각 차지하였다. 도시식생의 연간 순탄소흡수를 최대화하기 위해서 가로수의 전정과 공원 내 잔디깎기에 의한 탄소방출을 최소화할 식재계획 및 관리가 요구된다.