Publication

@article{bashyal2025open,

title = {Open innovation in industrial demand response: A computing continuum approach to overcoming technological barriers},

author = {Atit Bashyal and Pakin Veerachanchi and Tina Boroukhian and Hendro Wicaksono},

doi = {https://doi.org/10.1016/j.joitmc.2025.100678},

year  = {2025},

date = {2025-11-06},

urldate = {2025-11-06},

journal = {Journal of Open Innovation: Technology, Market, and Complexity},

volume = {11},

issue = {4},

pages = {100678},

publisher = {Elsevier},

abstract = {The rise of Industrial IoT (IIoT) alongside cloud, edge, and fog computing is transforming industrial operations and enabling new Demand Response (DR) opportunities in the Smart Grid. DR allows end-users to adjust energy consumption in response to external signals. Still, its adoption in industry is limited by challenges such as communication, security, interoperability, and computing constraints, especially in environments requiring real-time decision-making. This paper explores how the Computing Continuum can help overcome these barriers and support scalable, flexible, and responsive Industrial Demand Response (IDR) systems. We propose a reference architecture that integrates key IIoT and energy management trends to support real-time processing and system interoperability. A central focus is the role of aggregators and the importance of open innovation and cloud service providers in enabling adaptive and collaborative IDR solutions. Our findings offer a roadmap for aligning technological advancements with IDR needs, contributing to more effective and sustainable energy management in Industry 4.0 settings.},

keywords = {artificial intelligence, data management, data science, demand response system, energy management, green energy, industry 4.0, ontologies, sustainability},

pubstate = {published},

tppubtype = {article}

}

@article{boroukhian2025semantic,

title = {Semantic middleware for demand response systems: Enhancing data interoperability in green electricity management for manufacturing},

author = {Tina Boroukhian and Kritkorn Supyen and Christopher William Mclaughlan and Atit Bashyal and Tuan Pham and Hendro Wicaksono},

doi = {https://doi.org/10.1016/j.compind.2025.104354},

year  = {2025},

date = {2025-11-01},

urldate = {2025-01-01},

journal = {Computers in Industry},

volume = {172},

pages = {104354},

publisher = {Elsevier},

abstract = {Optimizing the consumption of green electricity across sectors, including manufacturing, is a critical strategy for achieving net-zero emissions and advancing clean production in Europe by 2050. Demand Response (DR) represents a promising approach to engaging power consumers from all sectors in the transition toward increased utilization of renewable energy sources. A functional DR system for manufacturing power consumers requires seamless data integration and communication between information systems across multiple domains, including both power consumption and generation. This paper introduces a semantic middleware specifically designed for DR systems in the manufacturing sector, using an ontology as the central information model. To develop this ontology, we adopted a strategy that reuses and unifies existing ontologies from multiple domains, ensuring comprehensive coverage of the data requirements for DR applications in manufacturing. To operationalize this strategy, we designed novel methods for effective ontology unification and implemented them within a dedicated unification tool. This process was followed by data-to-ontology mapping to construct a knowledge graph, and was further extended through the development of a querying system equipped with a natural language interface. Additionally, this paper offers insights into the deployment environment of the semantic middleware, encompassing multiple data sources and the applications that utilize this data. The proposed approach is implemented in multiple German manufacturing small and medium-sized enterprises connected to a utility company, demonstrating consistent data interpretation and seamless information integration. Consequently, the method offers practical potential for optimizing green electricity usage in the manufacturing sector and supporting the transition toward a more sustainable and cleaner future.},

keywords = {data management, demand response system, energy management, green energy, industry 4.0, industry 5.0, knowledge management, manufacturing, ontologies, semantic web, sustainability},

pubstate = {published},

tppubtype = {article}

}

@article{vijaya2025esgont,

title = {ESGOnt: An ontology-based framework for Enhancing Environmental, Social, and Governance (ESG) assessments and aligning with Sustainable Development Goals (SDG)},

author = {Annas Vijaya and Faris Dzaudan Qadri and Linda Salma Angreani and Hendro Wicaksono},

doi = {https://doi.org/10.1016/j.resenv.2025.100262},

year  = {2025},

date = {2025-08-25},

urldate = {2025-08-25},

journal = {Resources, Environment and Sustainability},

volume = {22},

pages = {100262},

publisher = {Elsevier},

abstract = {This study proposes ESGOnt, an ontology-based framework that aligns Environmental, Social, and Governance (ESG) management with Sustainable Development Goals (SDGs). ESGOnt addresses key challenges in sustainable resource governance systems and cross-sector interoperability by providing a unified structure for ESG and SDG integration. The framework was developed through a systematic methodology that combines a literature review, standardization of ESG and SDG relationships, development of an adaptable maturity model, and ontology implementation using established methods such as Methontology and NeOn. ESGOnt enables the integration of diverse ESG taxonomies and ESG reporting standards, including GRI and ESRS, and assists companies in their ESG performance evaluation. Empirical validation through real-world use cases demonstrates its capability to (1) direct assessment of ESG assessments with specific SDG targets, such as SDG13 (Climate Action) and SDG 12 (Responsible Consumption and Production), (2) assess organizational ESG progress through different metrics, (3) facilitation of standardized and interoperable reporting for small and large enterprises, and (4) automatically validate organization compliance with EU Non-Financial Reporting Directive regulations. The findings show that ESGOnt resolves data inconsistency and transparency issues by enabling integrated and auditable sustainability reporting. The ontology-driven approach of the framework enables scalable and policy-relevant tools for tracking environmental and social impacts, while its maturity model focuses on strategic improvements in resource efficiency. Future studies will analyze and extend ESGOnt’s functionality for sector-specific capabilities, such as bioeconomy control systems, and explore advanced AI-driven inspection methods for real-time ESG-SDG assessment.},

keywords = {data management, data science, ESG, interoperability, ontologies, semantic web, sustainability},

pubstate = {published},

tppubtype = {article}

}

@article{wicaksono2024artificial,

title = {Artificial-intelligence-enabled dynamic demand response system for maximizing the use of renewable electricity in production processes},

author = {Hendro Wicaksono and Martin Trat and Atit Bashyal and Tina Boroukhian and Mine Felder and Mischa Ahrens and Janek Bender and Sebastian Groß and Daniel Steiner and Christoph July and others},

url = {https://link.springer.com/article/10.1007/s00170-024-13372-7},

doi = {https://doi.org/10.1007/s00170-024-13372-7},

year  = {2025},

date = {2025-05-01},

urldate = {2025-05-01},

journal = {The International Journal of Advanced Manufacturing Technology},

volume = {138},

pages = { 247–271},

publisher = {Springer London},

abstract = {The transition towards renewable electricity provides opportunities for manufacturing companies to save electricity costs through participating in demand response programs. End-to-end implementation of demand response systems focusing on manufacturing power consumers is still challenging due to multiple stakeholders and subsystems that generate a heterogeneous and large amount of data. This work develops an approach utilizing artificial intelligence for a demand response system that optimizes industrial consumers’ and prosumers’ production-related electricity costs according to time-variable electricity tariffs. It also proposes a semantic middleware architecture that utilizes an ontology as the semantic integration model for handling heterogeneous data models between the system’s modules. This paper reports on developing and evaluating multiple machine learning models for power generation forecasting and load prediction, and also mixed-integer linear programming as well as reinforcement learning for production optimization considering dynamic electricity pricing represented as Green Electricity Index (GEI). The experiments show that the hybrid auto-regressive long-short-term-memory model performs best for solar and convolutional neural networks for wind power generation forecasting. Random forest, k-nearest neighbors, ridge, and gradient-boosting regression models perform best in load prediction in the considered use cases. Furthermore, this research found that the reinforcement-learning-based approach can provide generic and scalable solutions for complex and dynamic production environments. Additionally, this paper presents the validation of the developed system in the German industrial environment, involving a utility company and two small to medium-sized manufacturing companies. It shows that the developed system benefits the manufacturing company that implements fine-grained process scheduling most due to its flexible rescheduling capacities.



},

keywords = {artificial intelligence, data management, data science, demand response system, energy management, green energy, industry 4.0, interoperability, machine learning, manufacturing, ontologies, reinforcement learning, semantic web, sustainability},

pubstate = {published},

tppubtype = {article}

}