Abstract. The growing presence of renewable energy sources into manufacturing systems presents massive challenges in their intermittency and uncertainty, causing inefficiency in energy consumption and production planning. The paper introduces a smart energy management network of manufacturing systems that dynamically balances machine activities with the availability of renewable energy. The suggested solution consists of a predictive model of renewable generation and an optimization-based scheduling system to reduce the cost of energy, delays in production, and grid reliance. There is a multi-objective formulation that is created based on energy use, operating limitations and emission parameters. A smart decision-making policy, founded on machine learning-aided prediction and adaptive scheduling, is applied to guarantee real-time responsiveness when operating in different energy conditions. The framework is tested on a realistic load and renewable profile on a representative manufacturing scenario. Findings show that there are considerable increases in the use of renewable energy, decrease in peak grid demand, and general energy cost savings over traditional scheduling methods. The new methodology provides a scalable and viable approach to sustainable and energy-efficient smart manufacturing systems.
The growing share of renewables has introduced new operational problems into modern smart grids because the electricity from renewable energy sources is intermittent, the load demands are variable and the requirement for real-time control has arisen. In this paper, a framework for smart grid forecasting and scheduling...
Shafaqut Ali, R. Asif· Pakistan journal of engineer...· 0 citations
The increasing integration of distributed energy resources and flexible loads has transformed university campuses into complex energy systems that require coordinated operational strategies capable of managing renewable uncertainty while maintaining economic and environmental performance. This paper proposes a two-stag...
Edwin M. Garcia, C. Cuji, A. Aguila Téllez et al.· Sustainability· 1 citation
Effective industrial demand-side management can be attained by employing machine learning techniques that are straightforward and interpretable, in conjunction with domain-specific scheduling logic, as well as taking into account hourly price fluctuations and the time-dependent availability of solar energy.
Fouad Zaro· WSEAS Transactions on power...· 0 citations
The Machine Learning-Based Intelligent Energy Management System (ML-IEMS) proposed in this paper combines a hybrid CNN-LSTM model for short-term load and renewable generation forecasting with a Reinforcement Learning dispatch agent for real-time storage, demand response, and grid exchange scheduling.
N. Suganthi, S. Tamilselvan· ITM Web of Conferences· 0 citations
Microgrid systems are localized energy networks that integrate multiple energy sources, such as renewable energy generators, energy storage systems, and traditional power sources, to provide reliable and sustainable electricity supply. Effective energy management plays a crucial role in optimizing the utilization of th...
M. el-sseid, A. Alsharif, L. B. Ben Dalla et al.· Comprehensive Journal of Sci...· 0 citations
The use of renewable energy resources and distributed energy resources has added complexity to the energy management of smart microgrids, thus the importance of efficient demand response scheduling has come into play to guarantee energy grid stability, decrease operational costs, and enhance the usage of renewable ener...
B. Kumar, J. Ragaventhiran, G. Sharmila et al.· 2026 International Conferenc...· 0 citations
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