Publication

@article{bashyal2025unsupervised,

title = {Unsupervised domain adaptation framework for photovoltaic power forecasting using variational auto-encoders},

author = {Atit Bashyal and Chidambar Prabhakar Bangre and Tina Boroukhian and Hendro Wicaksono},

doi = {https://doi.org/10.1016/j.apenergy.2025.126606},

year  = {2025},

date = {2025-12-01},

urldate = {2025-01-01},

journal = {Applied Energy},

volume = {400},

pages = {126606},

publisher = {Elsevier},

abstract = {The global transition towards renewable energy sources necessitates accurate forecasts of such energy sources for efficient grid management. While deep learning models offer effective solutions for intermittent renewable energy forecasts, they face challenges due to their inherent data intensity. Transfer learning methods have emerged as valuable tools to address such challenges. However, existing transfer learning frameworks used in renewable energy forecasting, require a significant amount of labelled training data for fine-tuning and knowledge transfer, limiting their applicability to scenarios where abundant data are available. This paper introduces a domain adaptation framework that enables seamless knowledge transfer from forecasting models trained with abundant data to models that need to be trained without labelled data. The proposed domain adaptation framework, leverages variational inference techniques to align feature spaces between source and target domains, utilizing a generative variational auto-encoder architecture. Experimental validation across solar parks with varying configurations demonstrates the replicability and adaptability of the proposed method. This research underscores the enduring potential of domain adaptation in advancing photovoltaic power forecasting while providing valuable insights into overcoming challenges in transfer learning-based renewable energy forecasting.},

keywords = {energy management, green energy, machine learning, sustainability, transfer learning},

pubstate = {published},

tppubtype = {article}

}

@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{bashyal2025multi,

title = {Multi-agent deep reinforcement learning based demand response and energy management for heavy industries with discrete manufacturing systems},

author = {Atit Bashyal and Tina Boroukhian and Pakin Veerachanchai and Myanganbayar Naransukh and Hendro Wicaksono},

doi = {https://doi.org/10.1016/j.apenergy.2025.125990},

year  = {2025},

date = {2025-08-15},

urldate = {2025-01-01},

journal = {Applied Energy},

volume = {392},

pages = {125990},

publisher = {Elsevier},

abstract = {Energy-centric decarbonization of heavy industries, such as steel and cement, necessitates their participation in integrating Renewable Energy Sources (RES) and effective Demand Response (DR) programs. This situation has created the opportunities to research control algorithms in diverse DR scenarios. Further, the industrial sector’s unique challenges, including the diversity of operations and the need for uninterrupted production, bring unique challenges in designing and implementing control algorithms. Reinforcement learning (RL) methods are practical solutions to the unique challenges faced by the industrial sector. Nevertheless, research in RL for industrial demand response has not yet achieved the level of standardization seen in other areas of RL research, hindering broader progress. To propel the research progress, we propose a multi-agent reinforcement learning (MARL)-based energy management system designed to optimize energy consumption in energy-intensive industrial settings by leveraging dynamic pricing DR schemes. The study highlights the creation of a MARL environment and addresses these challenges by designing a general framework that allows researchers to replicate and implement MARL environments for industrial sectors. The proposed framework incorporates a Partially Observable Markov Decision Process (POMDP) to model energy consumption and production processes while introducing buffer storage constraints and a flexible reward function that balances production efficiency and cost reduction. The paper evaluates the framework through experimental validation within a steel powder manufacturing facility. The experimental results validate our framework and also demonstrate the effectiveness of the MARL-based energy management system.},

keywords = {artificial intelligence, data science, deep learning, demand response system, energy management, green energy, machine learning, manufacturing, operation research, reinforcement learning, 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}

}