Yarin Shohat’s Post

☀️ Enhancing Solar Efficiency with Hybrid PV/T Systems and Machine Learning. This study delves into a hybrid Photovoltaic/Thermal approach, optimizing machine learning models like Random Forest and Support Vector Regression to predict solar yield and water output. By optimizing hyperparameters through grid search, exceptional accuracy in solar yield prediction was achieved. These advancements demonstrate how data-driven techniques can revolutionize renewable energy systems, enhancing both solar power generation and thermal efficiency. Key Outcomes: > RF and Tuned RF Regression: Achieved the highest accuracy in cumulative solar yield with an R² score of 0.9952 and an RMSE value of 0.2583 using Grid Search CV > SVR and Tuned SVR Regression: Delivered the highest accuracy in PV output prediction with an R² score of 0.9935 and RMSE reduced to 0.5087 using Grid Search CV > Enhanced Performance of Other Models: Tuned XGBR, AdaBR, RR, EN, and L models showed dramatic improvements, though Linear Regression did not benefit from hyperparameter tuning. > Best Algorithm for PV and Water Productivity: The study suggests RF and Tuned Random Forest Regression as the most effective models for predicting solar PV power and water yield in hybrid PV/T systems. 💡 Explore the full open access study Hybrid photovoltaic/thermal performance prediction based on machine learning algorithms with hyper-parameter tuning by Karthikeyan Ganesan, SATHEESHKUMAR PALANISAMY, Valarmathi Krishnasamy, Ayodeji Salau, Vinoth Rathinam, & Sankar Ganesh Seeni Nayakkar here: https://lnkd.in/dscCHSnE #HyperparameterTuning #MachineLearning #GridSearch #PVTechnology #HybridPhotovoltaicThermal #RandomForest #SupportVectorRegression #IJSE Taylor & Francis Group Outliers of parameters

Discover critical factors for choosing the right solar dataset. A sub-optimal design of a large-scale power plant leading to a 1% lower yield can result in lost profitability in the range of millions of dollars over the asset's lifetime. With our MASTER approach, learn how to increase your profits, transparency, and productivity by choosing the right dataset for your project. Download the ebook "How to choose the right dataset for evaluation of solar projects" here: https://lnkd.in/eqc6Fajd What will you learn? -- Weighted vs mean approach -- These are the two most common approaches technical advisors use to justify estimates of solar resources. Find out which is better. -- Real vs synthetic data -- Developers often rely on synthetically generated data. Is it really safe to use such data? -- Comparison of providers -- See the differences between the top 7 solar and meteo data providers. -- 6 critical criteria -- With so many options to choose from, what to look for when selecting the right dataset? #SolarEnergy #DataScience #SolarData #SolarPowerAnalysis

“A sub-optimal design of a large-scale power plant leading to a 1% lower yield can result in lost profitability in the range of millions of dollars over the asset's lifetime.” Absolutely agree, the better the information, the less faulty is the energy budget.

Day 1: Live Project Session on Forecasting & Analyzing Renewable and Non-renewable Energy Generation and Demand Patterns using Machine Learning & Deep Learning 🌍🔋 Link to the registartion from: https://lnkd.in/dmffF_hk (also in comment section) Today, I kicked off an exciting live project series where we dive deep into energy generation trends and demand forecasting. The goal is to leverage machine learning and deep learning techniques to analyze and forecast both renewable and non-renewable energy patterns. 📝 Key Highlights from Day 1: - 📊 Data Exploration: We examined a dataset comprising 35,000+ entries with 29 features related to energy generation sources like biomass, solar, wind, and fossil fuels. I guided participants through the process of cleaning and preprocessing the data, addressing missing values, and understanding its structure. - 🔍 Feature Classification: We identified key categorical, continuous, and discrete features that will play a critical role in our analysis and forecasting tasks. - 📉 Visualization: With the help of seaborn and matplotlib, we plotted distributions of various continuous features, uncovering key insights into energy production trends across different sources. - 🧑💻 Dealing with Missing Data: We tackled missing values by filling in discrete features with the mode and continuous features using various imputation techniques. - 🔮 Next Steps: We are moving toward time series analysis, where the focus will be on predicting future energy demands and prices. The session was highly interactive, and participants got hands-on experience with real-world data, setting the foundation for building accurate forecasting models in future sessions. Stay tuned for more insights in the coming days! 💡 #MachineLearning #DeepLearning #EnergyAnalytics #Forecasting #DataScience #LiveProject #RenewableEnergy #NonRenewableEnergy #TimeSeriesAnalysis #DataVisualization #AIForEnergy #Sustainability #DataAnalysis

🌍 Diving into Global Energy Trends with Power BI: World Energy Consumption Analysis ⚡ I’m excited to share my latest interactive dashboard, developed using Microsoft Power BI, which provides in-depth insights into World Energy Consumption across different countries. This dashboard uncovers key trends and patterns in energy consumption, helping to understand how countries are utilising various energy sources. Project Title: > World Energy Consumption Analysis Data Source: > Quantum Analytics NG Key Performance Indicators (KPIs): > Biofuel Consumption Change (Sum) > Solar Electricity (Sum) > Gas Production Per Capita (Sum) > Wind Energy Per Capita (Sum) Key Insights: > Hydro Electricity by Country > Oil Production Trends by Year > Biofuel Electricity Capital by Country > Gas/Electricity Sharing across Countries > Biofuel Electricity Per Capita by Country Interactive Features: > Year Slicer to explore trends over time > Country Slicer to compare energy data across nations Special Thanks: A huge thank you to Quantum Analytics NG, Jonathan Osagie, Christian Okwara, and Abdullah Bankole, MCT MCDAA GMNSE for their invaluable support throughout this project! 🙌 This dashboard sheds light on global energy consumption trends, showcasing how countries are transitioning towards sustainable energy sources. From biofuel and solar electricity to wind and gas production, this tool provides insights for policymakers, researchers, and industry professionals. 💡 Let’s connect! I’d love to hear your thoughts on how data visualisation can provide deeper insights into energy consumption and sustainability efforts worldwide. Let’s explore new opportunities to harness the power of data together. #QuantumAnalyticsNG #JonathanOsagie #ChristianOkwara #AbdullahBankole #SustainableEnergy #GlobalEnergyTrends

Excited to Share My Latest Published Research! I’m thrilled to announce that my new scientific article, "A Minute-Resolution Downscaling Algorithm for High-Resolution Global Irradiance Time Series," has been published! 🎉 Along with my co-authors, Martin Andersson and Henrik Davidsson, we developed an algorithm to enhance renewable energy modeling and integration. This research is part of the Circular Fuels Project, dedicated to advancing renewable energy technologies to support sustainable aviation fuel (SAF) production. 💡 The core of our work bridges the gap between existing hourly irradiance datasets and the growing demand for sub-hourly precision in solar energy modeling. Our algorithm is simple, user-friendly, and computationally efficient, making it ideal for energy modeling frameworks. This approach is particularly relevant for applications impacted by rapid solar fluctuations, such as coupling photovoltaic systems with fast ramp rate technologies like electrolyzers. 🌟 Highlights of the Study: ·        Emphasizes the importance of high-resolution global solar irradiance datasets for diverse applications. ·        Addresses the need for precise time-matching between solar electricity generation and electrolysis consumption, improving frequency and balancing flexibility. ·        Develops a straightforward yet effective algorithm to downscale hourly solar irradiance data into minute-level resolution. ·        Validates the algorithm in locations with highly fluctuating solar irradiance, achieving daily-to-weekly accuracy under all sky conditions. If you’re interested in renewable energy, solar technology integration, or high-resolution optimization of large energy systems, I’d love to connect and hear your thoughts! 🚀 📄 Access the full paper here: https://lnkd.in/dCE5-hWB #RenewableEnergy #SolarEnergy #Photovoltaics #SolarToHydrogen

𝐓𝐡𝐞 𝐅𝐮𝐭𝐮𝐫𝐞 𝐨𝐟 𝐑𝐞𝐧𝐞𝐰𝐚𝐛𝐥𝐞 𝐄𝐧𝐞𝐫𝐠𝐲 𝐂𝐚𝐫𝐞𝐞𝐫𝐬: 𝐓𝐡𝐞 𝐈𝐦𝐩𝐚𝐜𝐭 𝐨𝐟 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 🌐 Today, let's delve into a topic that's reshaping the way we approach renewable energy: the role of data science. Data science isn't just a buzzword; it's becoming increasingly integral to the success of renewable energy initiatives. Here are five key points highlighting its significance in the future of renewable energy jobs: Predictive Analytics: Data science enables us to analyze vast amounts of data to predict energy demand, optimize resource allocation, and enhance overall efficiency in renewable energy projects. Smart Grid Optimization: By leveraging data analytics, we can optimize the performance of smart grids, improving energy distribution, reducing wastage, and enhancing grid reliability. Renewable Resource Forecasting: With advanced modeling techniques, data scientists can forecast renewable resource availability, such as solar irradiance and wind speed, enabling better planning and decision-making for energy generation. Asset Performance Monitoring: Data-driven monitoring and analysis allow for real-time assessment of renewable energy assets, leading to proactive maintenance, increased uptime, and optimized performance. Energy Market Insights: Data science provides valuable insights into energy market trends, price forecasting, and demand patterns, empowering renewable energy companies to make informed strategic decisions. The future of renewable energy jobs is intricately linked with data science, presenting exciting opportunities for professionals with expertise in this field. I'd love to hear your thoughts on this pivotal intersection of renewable energy and data science. How do you see data science shaping the future of our industry? Share your insights below! 📊🔋 ________________________________ Looking to navigate the dynamic world of renewable energy careers or seeking top talent for your organization? Let's connect and explore how we can drive innovation together! 📨 Email: randallproservices@gmail.com 📞 Phone: (585) 577 - 7870 #RenewableEnergy #DataScience #FutureOfWork #GreenTech #EnergyAnalytics

Have you imagined how #machine #learning could improve energy access? As we work towards a more sustainable future, the combination of machine learning and renewable energy systems is a game-changer. Why does machine learning matter in Renewable Energy? 1. Optimizing Energy Production: Machine learning algorithms can analyze large amounts of data from solar panels, wind turbines, and other renewable sources to predict energy output, helping us utilize our resources more efficiently. 2. Predictive Maintenance: By predicting equipment failures before they occur, Machine learning helps reduce downtime and maintenance costs, ensuring a more reliable energy supply. 3. Smart Grid Management: Machine learning enhances the efficiency of smart grids, allowing for real-time adjustments based on energy demand and supply fluctuations, thereby integrating more renewable energy sources seamlessly. 4. Energy Consumption Forecasting: With Machine learning, we can analyze historical data to forecast energy needs, optimizing the balance between supply and demand. 5. Environmental Impact: Machine learning helps assess and minimize the environmental impact of energy projects by analyzing data on emissions and resource use. In Africa, where renewable energy potential is vast yet underutilized, the integration of machine learning can catalyze a transformative shift toward sustainable energy solutions. What are your thoughts on the role of machine learning in renewable energy? Share your insights in the comments #RenewableEnergy #MachineLearning #linkedinAcceleratorwithlynn

Hello all! I'm excited to share about my dissertation: Renewable Energy Prediction using Weather Data 🌱⚡ The aim of my research is to explore how weather data can be used to accurately predict renewable energy generation, focusing primarily on wind and solar power. With the increasing shift towards sustainable energy, predicting energy output more reliably can optimize grid management and contribute to a more sustainable future. 💡 Data Sources: Historical weather datasets (e.g., temperature, humidity, wind speed, solar radiation) Renewable energy generation data from National Grid ESO 🔍 Techniques Used: Machine Learning models like Random Forest and XGBoost for model training Data preprocessing with normalization and feature selection for better model accuracy Correlation analysis to identify key weather parameters that impact energy output Looking forward to sharing the final results and insights soon! 🌞🌬️ #RenewableEnergy #MachineLearning #DataScience #Sustainability #ClimateTech #UniversityofLeicester

I am excited to present my 14th project with Quantum Analytics NG Project Title; WORLD ENERGY CONSUMPTION ANALYSIS Visualization Tool: Tableau Key Performance Indicators (KPIs); 🔸 Electricity Generated 🔸 Total Coal Consumption 🔸 Total Countries 🔸 Total Oil Consumption 🔸 Total renewable Consumption Insights Generated; 🔸 Top 10 Countries per GDP 🔸 Top 10 Hydro Electric Generating Countries 🔸 Top 10 Biofuel Electricity Generating Countries 🔸 Coal Production by Top 3 Countries 🔸 Top 3 Energy Per GDP Countries 🔸 Population by Country Blessing Olayemi Jonathan Osagie Quantum Analytics NG #tableau #tableaupublic #tableaujobs