EnAnalytica

#GNN #trading #finance #machinelearning #SCED #optimization #mathematics #renewables #investment #research

Chasing SCED and Binding Constraints Predictability via Distributed Optimization and Machine Learning... Insights coming soon.... #energy #trading #renewables #finance #investment #optimization #mathematics #research

#EnergyTransition #EnergyPlanning #EnergyMarkets #EnergyInnovation #Utilities #TSOs #AssetInvestors #EnergyTraders #FutureOfEnergy #RenewableEnergy #InformedDecisions

Power Trading Architecture => highlighting the importance of a holistic approach, combining mathematical programming, market simulation, and AI for effective energy trading. The modular design of algorithmic toolkit is essential in order to allow traders to gain a comprehensive view of the market. #MarketSimulation #PowerSystems #EnergyTrading #AI #Innovation #Investment #Strategy #Nodal

📢NEWS! We've cracked a major conundrum at EnAnalytica! 🎉 After lots and lots of months of intense research and development, we're thrilled to announce a significant breakthrough in Security-Constrained Economic Dispatch (SCED) modeling from a trading perspective. We like to call it beat the clock approach! This potentially could be a game-changer for traders in nodal electricity markets. Stay tuned next week for videos showcasing the multilateral aspect of our new approach. Very soon we will know what could be the potential impact of this very different approach to SCED! #SCED #ElectricityMarkets #AI #Innovation #EnergyTrading #Power #Finance #Investments #Mathematics #Optimization #Renewables #Strategy

Why XGBoost is powerful? Imagine you are trying to find the bottom of valley and you can see the curvature of the terrain... So XGBoost => - It uses a second order Taylor as approximation of the loss function. Thus it consider both 1st and 2nd order gradient => so we have curvature.... - Hessian, as usual, provides info about curvature of the loss function => so XGBoost makes more informed steps during optimization, converging faster and potentially finidning better minima then gradient only based methods. - We all know that Fisher information matrix is related to the expected curvature of the log-likelihood => it measure the amount of information that an observation carries about an unknown parameter. - XGBoost and the Hessian it uses => local approximation in layman terms... - Flat manifold in information geometry is the Euclidean geometry manifold, so distance and angles apply... XGBoost is trying to find a region where the loss function behaves more like a quadratic function, and we all know that optimization likes quadratic functions... So XGBoost is effectively choosing the path minimizes the "geodesic distance" #MachineLearning #DataScience #Optimization #Energy #Finance #PortfolioManagement #Trading #XGBoost

#trading #finance #forecasting #energy #renewables #optimization #research #science #enanalytica

#energy #forecasting #power #optimization #research #enanalytica #finance #investment #weather

Unveiling the Magic Behind Decisions: The Power of Latent Variables Ever wondered how traders make split-second decisions, or how AI can generate incredibly realistic images? The secret lies in latent variables - hidden patterns that shape our world. From understanding market trends to creating cutting-edge AI models, latent variables are the key to unlocking insights and driving innovation. Let's explore basics of this simple and at the same time fascinating concept together! #AI #MachineLearning #DataScience #LatentVariables #DecisionMaking #Mathematics #Trading #Finance #Optimization #Power #Energy #Investment #Markets #Strategy