EEPower’s Post

To the question of why engineers have difficulty with Machine Learning: Why would the mechanical engineering world employ ML in a project such as this? This is the making and building of a physical thing, an innovative solar cell. Is it a use case for ML? I don’t see it.

This paper proposes a #prediction-#based #adaptive #duty #cycle (#PADC) MAC protocol called PADC-MAC, that incorporates current and future harvested energy information using the mathematical formulation to improve #energy #harvesting #based #wireless #sensor #networks (EH-WSNs) performance. Furthermore, a machine learning model, namely a nonlinear autoregressive (NAR) neural network, is employed that achieves good prediction accuracy under dynamic harvesting scenarios. ---- Micheal Drieberg, Kishore Bingi More details can be found at this link: https://lnkd.in/gvGQRSXx

Please find our latest conference paper online. Abstract: High penetration of low-inertia Renewable Energy Sources (RESs) like wind and solar in power systems results in frequency stability issues. This necessitates the deployment of a Frequency Containment Reserve (FCR) to ensure frequency stability. The optimal deployment of FCR necessitates accurate inertia forecasts. Machine learning models like ANN are used for inertia forecasting. However, the accuracy of these models highly relies on their hyperparameters. This necessitates a suitable hybrid model to improve the forecasting accuracy. A hybrid model uses a machine learning algorithm to obtain forecasts and hyperparameters of the model are optimized by a suitable metaheuristic algorithm. Further, the missing values in the input data significantly affect the forecasting accuracy. The missing values can be easily replaced by a suitable data preprocessing technique. Therefore, this paper proposes two novel hybrid inertia forecasting models (ANN_ WaOA-CleanTS and SVR_ WaOA-CleanTS) using two powerful machine learning forecasting algorithms (ANN and SVR), a new metaheuristic optimization algorithm, Walrus Optimization Algorithm (WaOA), and a data preprocessing tool (cleanTS). ANN and SVR models are capable of obtaining accurate inertia forecasts due to their capability to identify nonlinear patterns in the data. The WaOA shows superior performance compared to other optimization techniques in various forecasting problems. The proposed work uses an R package, cleanTS, to replace the missing values as it shows superior performance in various forecasting problems. The performance analysis shows that the proposed hybrid models have an annual inertia forecasting accuracy above 97%.

This technology, if it is feasible, will change the energy landscape forever. I wonder how the energy market will react on this technology. https://lnkd.in/gefMdV3X

Proven in prototype with a "spintronic p-bit," this new approach to computing could deliver a three-orders-of-magnitude energy saving.

If you're interested in learning about the application of machine learning to power systems, be sure to read my latest research paper: #machinelearning #aritificialintelligence

I am thrilled to announce that our latest research paper on Electric Vehicle (EV) load forecasting has been published in the Energy Journal, and I would like to extend my gratitude to the coauthors, Ward Somers, Kevin de Bont,Shalika Walker, PhD , Joep van der Velden and Wim Zeiler, for their invaluable contributions. This research was a collaborative effort between OpenToControl, Eindhoven University of Technology and Kropman, representing a significant milestone in the dynamic field of EV integration and load forecasting.    # Unlocking EV Potential while Ensuring Grid Stability   In an era of rapid EV adoption, the integration of electric vehicles into the existing power grid has become an increasingly complex challenge. Recognizing the importance of this issue, our research tries to seamlessly blend the best of both academic and industry expertise, delving into the pivotal task of accurate load forecasting.  Our study uncovers the crucial factors that influence the precision of EV load forecasting. For smaller-scale analyses, user calendar information is more important, while larger scenarios find "previous week's power," "hour of the day," and "number of connections" wielding more significant influence. Furthermore, our research highlights the superiority of aggregated forecasting over individual charging piles, underscoring the collective impact of EVs on the grid. We also worked on uncertainty estimation within forecasting models, revealing that as the number of EVs increases, the reliability of uncertainty estimates also grows. As the adoption of EVs continues to accelerate, our collaborative work, is an example of the incredible potential when academia and industry join forces. We anticipate that our research will serve as a starting step towards a smarter, more efficient energy management for a cleaner and greener future.    📚 For those interested in our research kindly use the link below to read the paper. Together, let's drive progress and innovation! 🚀 #ElectricVehicles #EnergyTransition #LoadForecasting #OpenToControl #Research #Innovation #EnergySolutions.  https://lnkd.in/eKyZpFMP

I am happy to share our latest contribution, published in Separation and Purification Technology: "Optimizing CO2 Capture in Pressure Swing Adsorption Units: A Deep Neural Network Approach with Optimality Evaluation and Operating Maps for Decision-Making." This achievement wouldn't have been possible without the dedication and insight of Carine Rebello. Her contributions were pivotal to the success of this research at the Department of Chemical Engineering - NTNU. Mentoring and collaborating with such a talented scientist has been a privilege. A special thanks to SUBPRO-Zero and its partners for promoting and funding this research. Key Highlights of Our Research: 🚀 Phenomenological Modeling for PSA Units: We've developed an in-house phenomenological model, enhancing our simulation capabilities and deepening the understanding of these intricate systems. 🧠 AI-Driven Environmental Innovation: Our work utilizes deep learning to create a surrogate model, significantly reducing the computational demands associated with the complexity of the original phenomenological model and keeping the optimization robustness. 💡 Optimality Evaluation Strategy: We introduce a novel strategy for assessing the optimality of surrogate-based optimization, streamlining the computational process without sacrificing accuracy. 🔍 Advanced Decision-Making Tools: The research provides new operating maps and optimality evaluations, delivering critical insights that improve process performance and efficiency. This publication marks a step forward in our ongoing efforts to tackle environmental sustainability challenges through technological innovation. #EnvironmentalTech #Innovation #CarbonCapture #AIResearch #ProudMentor #SubproZero #NTNU #PressureSwingAdsorption

🎉 I am excited to share that I’ve co-authored my first research paper “Multi-Agent Reinforcement Learning for Power Grid Topology Optimization” together with Alessandro Zocca and Sandjai Bhulai. Our preprint is available on ArXiv: https://lnkd.in/eN6nZg23. In modern power systems, adaptively changing the network topology using line-switching and bus-splitting actions is an under-utilized yet very cost-effective strategy for network operators facing rapidly changing energy patterns and contingencies. To navigate this complex and large combinatorial action space, we propose a hierarchical Multi-Agent Reinforcement Learning (MARL) framework that leverages the power grid’s inherent hierarchical nature. The inherent scalability of this approach makes it a promising AI tool for assisting network operators in their real-time decision-making and operations. Looking forward to delving deeper into this exciting field! #Research #MARL #PowerNetworks #PowerGridReliability #TopologyOptimization #ReinforcementLearning

"Vanadium redox flow batteries are one of the most promising energy storage technologies today due to their low fire risk, long cycle life, and excellent scalability. However, in order to unlock the technology’s full potential, further effort is needed toward the development of advanced control strategies." "In order to enhance the stability and anti-interference ability of vanadium redox flow batteries in microgrids, a group of researchers led by the University of Western Australia have developed a novel learning-based data-driven H∞ control approach" #flowbatteries #energy #electricity #energystorage #energytransition #energypolicy #infrastructure #transmission #distribution #resiliency #energysecurity