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{fekete2025comprehensive,

title = {A comprehensive causal AI framework for analysing factors affecting energy consumption and costs in customised manufacturing},

author = {Tamas Fekete and Isabella Marquez Petrone and Hendro Wicaksono},

url = {https://hendro-wicaksono.de/a-comprehensive-causal-ai-framework-for-analysing-factors-affecting-energy-consumption-and-costs-in-customised-manufacturing-2/},

doi = {https://doi.org/10.1080/00207543.2025.2580541},

year  = {2025},

date = {2025-10-29},

urldate = {2025-10-29},

journal = {International Journal of Production Research},

pages = {1–38},

publisher = {Taylor & Francis},

abstract = {The manufacturing sector is a major energy consumer, resulting in high operational costs and environmental impacts. In customised manufacturing, optimising energy use is especially challenging due to high variability and complex interdependencies between process factors. Meanwhile, the increasing availability of operational data presents opportunities for advanced analytics. Unlike traditional machine learning, which identifies correlations, causal AI uncovers cause-and-effect relationships – enabling more explainable and actionable decision-making. This paper presents a causal AI framework that combines causal discovery and inference methods to analyse drivers of energy consumption and process duration in customised manufacturing. We integrate three core components: DirectLiNGAM and RESIT for causal discovery, and DoWhy for causal inference. Applied to a real-world case study in a German energy-intensive manufacturing Small and Medium-sized Enterprise (SME), the framework demonstrates its ability to identify key causal drivers of inefficiency and energy use. Results show improved interpretability, revealing, for example, that increasing product weight can reduce energy consumption by up to 4.70 kWh per unit, enabling targeted, data-driven interventions for optimisation. Compared to correlation-based models, the framework reveals underlying causes, helping decision-makers focus on critical levers for sustainability and cost reduction. The findings lay a foundation for applying causal AI in industrial settings through a structured, data-driven approach.},

keywords = {artificial intelligence, causal AI, causal inference, energy management, explainable AI, industry 4.0, industry 5.0, machine learning, manufacturing, 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}

}

@article{nokey,

title = {From Theory to Practice: Implementing Causal AI in Manufacturing for Sustainability},

author = {Heonyoung Jeong and Tamas Fekete and Atit Bashyal and Hendro Wicaksono },

url = {https://www.sciencedirect.com/science/article/pii/S1877050925002194},

doi = {https://doi.org/10.1016/j.procs.2025.01.211},

year  = {2025},

date = {2025-02-25},

journal = {Procedia Computer Science},

volume = {253},

pages = {1495-1504},

abstract = {The use of AI in industry is increasingly popular, but its black-box nature poses decision-making challenges due to the lack of understanding of how variables influence each other. Causal AI addresses this by studying cause-and-effect relationships in the data. This paper explores applying causal AI in industry through a case study of CNC machines, which are significant in manufacturing and consume large amounts of energy. Industry 4.0 is transforming manufacturing, with CNC machines generating vast data analyzed by often opaque machine learning methods. Causal AI can uncover and quantify causal relationships between variables, aiding decision-making. Our case study uses CNC power consumption data to demonstrate causal AI in manufacturing, with existing models verifying our methodology. Future studies should extend our research to include variables without existing models, such as human habits. This case study serves as a starting point for other researchers, facilitating similar studies on complex data.},

keywords = {artificial intelligence, causal AI, causal inference, energy management, manufacturing, sustainability},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Demand response based industrial energy management with focus on consumption of renewable energy: a deep reinforcement learning approach},

author = {Atit Bashyal and Hani Alnahas and Tina Boroukhian and Hendro Wicaksono},

url = {https://www.sciencedirect.com/science/article/pii/S1877050925002145},

doi = {https://doi.org/10.1016/j.procs.2025.01.206},

year  = {2025},

date = {2025-02-25},

journal = {Procedia Computer Science},

volume = {253},

pages = {1442-1451},

abstract = {Integrating Renewable Energy Resources (RESs) into power grids requires effective Demand Response (DR) programs. Despite high DR potential in industrial sectors, adoption lags behind that of residential and commercial sectors due to diverse operations and production continuity requirements. This paper explores a reinforcement learning (RL)-based DR scheme for energy-intensive industries, promoting the consumption of distributed Renewable Energy (RE) generation. Our approach introduces modifications to the existing Markov Decision Process (MDP) framework. It proposes a flexible reward structure that provides flexibility in balancing production requirements and promotes the consumption of RE. This study addresses the gap in industrial DR literature, emphasizing tailored DR solutions for industrial settings. The key highlight of our RL-based DR solution is its ability to facilitate a price-based DR scheme while promoting the integration of RE into the smart grid.

},

keywords = {artificial intelligence, demand response system, energy management, manufacturing, reinforcement learning},

pubstate = {published},

tppubtype = {article}

}

@article{Shah2024,

title = {Leveraging Machine Learning for Power Consumption Prediction of Multi-Step Production Processes in Dynamic Electricity Price Environment},

author = {Muhammad Abdullah Shah and Hendro Wicaksono},

url = {https://www.sciencedirect.com/science/article/pii/S2212827124012332},

doi = {https://doi.org/10.1016/j.procir.2024.10.080},

year  = {2024},

date = {2024-11-27},

urldate = {2024-11-27},

journal = {Procedia CIRP},

volume = {130},

pages = {226-231},

abstract = {Rising energy costs drive a compelling demand for energy-efficient manufacturing across sectors, paralleled by increasing consumer preferences for eco-friendly products. To remain competitive, companies are actively enhancing their energy efficiency. Integrating dynamic pricing in manufacturing, aimed at optimizing renewable energy use, requires strategic adjustments in production planning for sustainability. This research highlights the importance of incorporating dynamic pricing into production planning, emphasizing the need to shift processes to time slots when the energy prices are low or optimal. This study focuses on predicting the power consumption of multi-step CNC machine operations within a production cycle. Utilizing advanced Machine Learning (ML), including neural networks, statistical, and additive models, this research found unique time series characteristics influencing model performance across production steps. A practical use case within a German manufacturing Small and Medium Enterprises (SME) demonstrates how prediction results can optimize production processes in a dynamic pricing environment, providing a blueprint for diverse machinery forecasting models. This research’s insights extend to any industry managing production schedules for multiple machines with various steps in a process cycle. Industries with high energy consumption will benefit significantly through aligning operational efficiency with environmental sustainability goals.},

keywords = {energy management, machine learning, manufacturing, sustainability},

pubstate = {published},

tppubtype = {article}

}

@article{thapaliya2024power,

title = {Power Consumption and Processing Time Estimation of CNC Machines Using Explainable Artificial Intelligence (XAI)},

author = {Suman Thapaliya and Omid Fatahi Valilai and Hendro Wicaksono},

url = {https://www.sciencedirect.com/science/article/pii/S1877050924000863},

doi = {https://doi.org/10.1016/j.procs.2024.01.086},

year  = {2024},

date = {2024-03-20},

urldate = {2024-03-20},

journal = {Procedia Computer Science},

volume = {232},

pages = {861–870},

publisher = {Elsevier},

abstract = {Due to environmental issues such as climate change, companies are required to optimize their resource and energy consumption in their production process. Predicting power consumption and processing time of all production facilities is essential for manufacturing to develop mechanisms to prevent energy and resource waste and optimize their use. Machine learning is a powerful tool for prediction tasks using data in digitalized environments. In this paper, we present power consumption and processing time prediction of CNC milling machines using five machine learning regression models, i.e., decision tree, random forest, support vector regression (SVR), extreme gradient boosting (XGBoost), and artificial neural network (ANN). Since most of those models are black-box, we applied two explainable artificial intelligence (XAI) approaches, SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME), to give post-hoc explanations of the predictions given by the machine learning models. Our experiments indicated that random forest regression performed the best in predicting power consumption and processing time. The explanation showed that the number of axis rotations and the number of travels to the machine's zero point in rapid traverse were the most important factors that affected the processing time and power consumption. The companies using CNC milling machines can use our prediction models to optimally plan and schedule the operation of the milling machines in a time and energy-efficient manner. They can also optimize the factors that affect power consumption and processing time the most.

},

keywords = {energy management, explainable AI, machine learning, manufacturing},

pubstate = {published},

tppubtype = {article}

}

@article{wicaksono2021accelerating,

title = {Accelerating Energy Transition to Green Electricity through Artificial Intelligence},

author = {Hendro Wicaksono},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

publisher = {OSF Preprints},

keywords = {artificial intelligence, energy management, machine learning, ontologies, semantic web, sustainability},

pubstate = {published},

tppubtype = {article}

}