Cell Press’ Post

Excited to share that the paper I worked on has been published in Energy and AI! In this paper a novel Home Energy Management System for a battery is proposed, coupling a Deep Reinforcement Learning agent with load forecasting done by a CNN-LSTM neural network. Some highlights of the paper: - Several reinforcement learning agents are trained with different algorithms (Double DQN, Dueling DQN, Rainbow and Proximal Policy Optimization). - The reinforcement learning agents solutions are compared to an optimization-based agent (MILP formulation). - The Proximal Policy Optimization agent was the one that obtained the best results. - Reinforcement Learning agents can reach a 35% reduction in electricity bill when compared to standard model-based agents. Paper: https://lnkd.in/diBZsk_Y #DeepLearning #ReinforcementLearning #Sustainability #EnergyManagementSystem

Ever wondered 🧐 how artificial intelligence (AI) and machine learning (ML) are set to revolutionize weather forecasting? 🤖 I delved into some cutting-edge research that sheds light on exactly this. But let’s be honest, wading through a 45-page scientific paper might not be at the top of your to-do list, especially if you're navigating the fast-paced world of energy trading ⏳📈. So, I’ve got you covered! Over the next few days, I’ll be breaking down the key insights from this paper into digestible, impactful bits specifically tailored for us in the energy sector. Stay tuned for how AI and ML can be your next game-changer in forecasting and trading strategies... #AI #ML #WeatherForecasting #EnergyTrading https://lnkd.in/edy8inpn

Scientists from Los Alamos National Library are utilizing AI to help them research cushion gas scenarios. Here's how this research can further the clean-energy economy. #AI #ML #futurism #IntelligenceFactory #digitaltransformation #DX https://lnkd.in/gk-9RpPx

Scientists from Los Alamos National Library are utilizing AI to help them research cushion gas scenarios. Here's how this research can further the clean-energy economy. #AI #ML #futurism #IntelligenceFactory #digitaltransformation #DX https://lnkd.in/g2YWCJf8

I recommend that new paper SOLAR ENERGY FORECASTING WITH DEEP LEARNING TECHNIQUE

Mastering Backpropagation for Smart Grid Optimization at Benel Energy Resources Hello, energy innovators! Today, let's explore the profound impact of backpropagation in neural networks and how we can utilize this powerful technique to enhance smart grid optimization and operational efficiency. Understanding Backpropagation Backpropagation is a pivotal algorithm in neural network training, enabling the network to learn from errors and improve over time. It adjusts the weights of the network to minimize the difference between predicted and actual outputs, refining the model's predictive accuracy. Key Concepts of Backpropagation 1. Error Propagation: Calculate the error at the output layer and propagate it backward through the network. This helps in identifying which weights need adjustment to reduce the error. 2. Gradient Calculation: Compute the gradient of the loss function with respect to each weight using partial derivatives. This provides a measure of how much each weight contributes to the overall error. 3. Learning Rate: Apply a learning rate to control the size of weight updates. A well-chosen learning rate ensures steady convergence to an optimal solution without overshooting. 4. Iterative Refinemen: Continuously update the weights through multiple iterations, gradually reducing the error and improving the model's performance. Applications of Backpropagation in Smart Grid Optimization** 1. Load Balancing: Train neural networks with backpropagation to predict and balance load across the smart grid. This ensures efficient energy distribution and minimizes the risk of outages. 2. Demand Response: Optimize demand response strategies by predicting consumption patterns and adjusting supply accordingly. Backpropagation helps in fine-tuning models that forecast demand fluctuations. 3. Fault Detection and Diagnosis: Implement neural networks for real-time fault detection and diagnosis in the smart grid. Early identification of faults allows for prompt corrective actions, enhancing grid reliability. 4. Energy Storage Management: Use neural networks to manage energy storage systems more effectively. Accurate predictions of energy demand and supply enable optimal charging and discharging cycles, maximizing storage efficiency. Cultivating AI Expertise Foster a culture of continuous learning and innovation at Benel Energy by encouraging team members to deepen their expertise in neural networks and backpropagation. Join the Backpropagation Journey Join us on our journey to master backpropagation and revolutionize smart grid optimization at Benel Energy. Together, we'll leverage advanced AI techniques to drive efficiency, reliability, and sustainability in our energy systems. #Backpropagation #NeuralNetworks #SmartGrid #AI #DataScience Let's optimize our smart grid operations through the power of backpropagation, ensuring a future of energy efficiency and reliability!      

I am excited to share that our work on wind speed and wind power forecasting has been published in Frontiers in Energy Research. We developed a method that combines the power wavelet decomposition principles and deep learning to deliver accurate short-term forecasts of wind speed and wind power on wind farms. The method is tested on three large datasets that have been made publicly available. All computer code used in this work is open source. Links to the code and data are available in the paper. I owe a debt of gratitude to my co-authors for the time and effort put into this work. Adaiyibo Kio, Jin Xu , Natarajan Gautam, and Yu Ding. "Wavelet Decomposition and Neural Networks: A Potent Combination for Short Term Wind Speed and Power Forecasting." Frontiers in Energy Research 12: 1277464. The paper is open access at https://lnkd.in/eKASej8N.

#mdpienergies #highlycitedpaper Capacity Estimation of Lithium-Ion Batteries Based on Multiple Small Voltage Sections and BP Neural Networks 👉 https://brnw.ch/21wMm3z #capacityestimation #lithiumionbatteries #multiplevoltagesections #neuralnetwork #BoxCoxtransformation

🚨 New Paper Alert 🚨 I am excited to share that our latest research on enhancing lithium-ion battery safety has been published in the Journal of Power Sources by Elsevier. In this work, we've developed a novel framework combining machine learning and multiphysics modeling to predict thermal runaway. By integrating graph neural networks and LSTM, we can accurately detect potential thermal hotspots, advancing battery safety. Please read and share via: https://lnkd.in/gjxnSFUF This paper is an extension of our previous work titled "A combined multiphysics modeling and deep learning framework to predict thermal runaway in cylindrical Li-ion batteries" which is accessible from the Journal of Power Sources via: https://lnkd.in/g-25exiN

⚙️ Intelligent #renewableenergy management software   #HY4RES 2nd workstream focuses on energy forecasting following energy production and users’ #energydemand   How? Using algorithms, data analytics, neural networks, machine & deep learning, real time data 💻   ➡️ Highlighting energy management scenarios – #hybridenergy use with #storage, self-managed…   #hydropower #solarenegery #windpower #greentransition #renewableenergy #dataanalytics #bigdata #deeplearning #machinelearning #innovation