National Innovation Centre for Data’s Post

🚀 Proud Supervisor Moment! 🎉 Huge congratulations to my recently graduated PhD student, Dr. Abdul A., for his recent publication in Energy Reports (Elsevier, 4.7 IF). His article, titled "Transfer-learning enabled adaptive framework for load forecasting under concept-drift challenges in smart-grids across different-generation-modalities," tackles the critical issue of load forecasting in smart grids, offering a novel solution to address concept-drift challenges. This work is a significant advancement in smart grid research and energy management. I encourage the research community to dive into this insightful paper. 🔗 Read the full article here: https://lnkd.in/eTd32G8x #Research #EnergyReports #SmartGrids #TransferLearning #Sustainability #Innovation #PhDAchievements

🤓 Exciting News! 🎉 I'm thrilled to share that my article, "Artificial Intelligence Powered Optimization of Photovoltaic Systems: Evaluating Maximum Power Point Tracking Approaches for Optimal Performance in Variable Environmental Conditions", has been published by Springer Nature in the Process Integration and Optimization for Sustainability journal! 📚 A big thank you to Springer Nature for providing this platform to share our research and for recognizing our work's significance in advancing the photovoltaic systems optimization field. 🌞 You can read the full article here: ✅ it's available in Springer Link: 🌐 https://lnkd.in/eidKptJA ✅ it's available also on ResearchGate: 🌐 https://lnkd.in/eCeHepmT DOI: 10.1007/s41660-024-00430-6 📖 I'm looking forward to continuing this journey of exploration and innovation and am already excited about the next research adventure. 🛤️ #Research #Sustainability #PhotovoltaicSystems #ArtificialIntelligence #RenewableEnergy #Optimization #SpringerNature

We are inviting Proposals for book chapters for an Edited Book on Transformation in Electricity Markets. A reputable international publisher will publish the book, and it will be indexed in Scopus after publication. The proposed areas of work could include Electricity Market Analysis and Economics, Electricity Forecasting, Uncertainty Modelling, Energy system Optimization, Renewable energy integration, Distributed Energy Resources, Artificial intelligence and machine learning approaches, Enabling technologies for future markets, Intelligent electricity systems, Data-Driven Analytics in Electricity Markets, AI and Machine Learning, Role of Energy Storage, Analytics for Storage Management, Electricity Trading, Risk Management and Market, Opportunity Analysis, Regulatory Compliance and Reporting, Enhancing Customer Satisfaction through Personalization and Tailored Services and Energy Management Tools. Those interested can send me a brief proposal in about 500 words to drvinaykandpal@gmail.com. The last date for submission of the proposal is 4th April 2024 For clarification, feel free to drop me a message through LinkedIn. #environmentalsustainabilty #climatechange #renenewablenergy #energytransition #circulareconomy #riskmanagement #pricing #extendedproducerresponsibility

We're happy to share our latest publication entitled "Spectral-temporal convolutional approach for PV systems output power forecasting: Case studies in single-site and multi-site" which has published at the Sustainable Energy, Grids and Networks journal (Elsevier). This study introduces several challenges, and one of the most significant goals is to capture the correlation between data series (spatial feature) and temporal correlation within each series (temporal feature) simultaneously. https://lnkd.in/g-_JGyF2

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%.

🌐 Advancing Energy Extraction with Support Vector Machines (SVM) 🌐   Hello LinkedIn community! Today, I'm excited to delve into the transformative potential of Support Vector Machines (SVM) in the realm of energy extraction and beyond.   🚀 Harnessing SVM for Enhanced Decision-Making Support Vector Machines (SVM) represent a cutting-edge tool in the arsenal of data-driven decision-making. These powerful algorithms excel in classification and regression tasks, offering unparalleled accuracy and versatility in analyzing complex datasets.   📊 SVM in Practice In the context of energy extraction, SVM can revolutionize various aspects of operations. From predicting reservoir properties to optimizing drilling parameters and identifying equipment failures, SVM empowers decision-makers with actionable insights derived from data analytics.   🔍 Driving Efficiency and Safety In an industry where precision is paramount, SVM enables operators to make informed decisions that enhance both efficiency and safety. By leveraging historical data and real-time monitoring, SVM aids in mitigating risks, minimizing downtime, and optimizing resource utilization.   🌍 Shaping the Future of Energy As we navigate towards a more sustainable and efficient energy future, the role of SVM becomes increasingly pivotal. By harnessing the power of machine learning and data science, SVM contributes to driving innovation, improving operational performance, and maximizing resource extraction while minimizing environmental impact.   💡 Embracing Technological Innovation The adoption of advanced decision-making models like SVM underscores a fundamental shift towards data-driven practices in the energy sector and beyond. By embracing technological innovation, organizations can unlock new opportunities, tackle challenges, and pave the way for a smarter, more resilient energy landscape.   🔗 Join the Conversation I invite you to join us in exploring the transformative potential of Support Vector Machines (SVM) and other cutting-edge technologies in shaping the future of energy extraction. Let's collaborate, share insights, and drive positive change towards a sustainable energy future!   #DataScience #MachineLearning #EnergyExtraction #SVM #Innovation #DecisionMaking

In the domain of utilities and energy, now more than ever it is critical that strategy and innovation are fostered with a long-term outlook. When integrating data and technology, it can be tempting to move quickly to keep up with industry trends. However, these investments *must* be sustainable and in order to do that, safety and privacy must be incorporated when decisions are made. This work is cutting edge in that many utilities are discussing how to expedite and scale the integration of data into their demand response tactics, but I haven't heard much rumble at the nexus of how privacy comes to play in this equation. Such a unique and interesting read - the transfer of existing frameworks is truly interesting! Great work as alway from the brilliant minds at Centre for Net Zero (Octopus Energy Group), Massachusetts Institute of Technology, University of Oxford, & Georgia Institute of Technology

There’s a lot of buzz about how AI is and will be transforming the world but, the massive impact it has on the energy infrastructure is not talked about enough. For context, a simple google search is estimated to consume 0.3 watt-hours whereas, a ChatGPT query could take up to 2.9 watt-hours (almost ten times as much!) of energy. And this is only the beginning, as there are more powerful AI tools being built with much higher energy requirements. This white paper titled "Powering Intelligence: Analyzing AI and Data Center Energy Consumption" from EPRI deep dives into the current and future energy trends of data center development and proposes a few key strategies to address the ever-increasing energy demand for AI data centers: 1. Enhancing efficiency in hardware and implementing efficient cooling solutions. (Cooling systems typically consume 40% of the total energy required in a data center) 2. Utilizing clean onsite or nearby energy sources (nuclear, renewable) coupled with BESS which can be used a grid resource if needed. 3. The data center-grid relationship should transition from a ‘passive load’ model to a ‘shared energy economy’ model with grid resources powering data centers and data center back up resources contributing to grid reliability. 4. Better forecasting and planning for future growth; constructing and bringing large data centers online could typically take 2-3 years, whereas the supporting energy infrastructure could take four or more years. For those interested, here’s the link to the paper:

Designing linear infrastructure like powerlines, cables and pipelines shouldn’t be a fragmented and laborious process. Optioneer is here to change that. Continuum Industries began in 2016 as a university project, and today, we're at the forefront of infrastructure planning. Led by a passionate team of engineers, mathematicians and creatives, our mission is to accelerate the world’s transition to renewable energy. Our AI-powered platform leverages GIS data and engineering knowledge to optimise the development process, freeing up professionals to focus on what they do best. Watch the video 🎥 #Infrastructure #AI #Innovation

Free Webinar Alert 🔔: Best-practice examples of digitalized power generation. We have some great news. A new energy sectored webinar is approaching. This gives you the chance to learn how AI and self-service analytics complement each other to get the most out of the digitalization of today. By joining this FREE webinar, you will learn:  🏷 Practical examples to show how leading players in hydropower and thermal power generation are solving challenges with AI.  🏷 How AI can reduce the workload for analyses by 90% and save many hours of precious expert time 🏷 What options are available to give engineers access to advanced analytics without programming and data science. 🏷 Which solutions leading companies use to solve issues in maintenance and operations. Why not secure your place today? Register now: https://lnkd.in/eNQei-iG 📅16th April 2024 / 3PM London / 10am GMT Dr. Harald Piringer- CEO and Co-founder Visplore Dr. Thomas Mühlbacher- CTO and Co-founder Visplore #WEBINAR #ENERGY #TECHNOLOGY #AI