<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>stochastic approximation on Chiara Camerota</title><link>https://camerotachiara.github.io/tags/stochastic-approximation/</link><description>Recent content in stochastic approximation on Chiara Camerota</description><generator>Hugo -- 0.114.0</generator><language>en</language><atom:link href="https://camerotachiara.github.io/tags/stochastic-approximation/index.xml" rel="self" type="application/rss+xml"/><item><title>Load Profile Generation for Robust Optimization: A Stochastic Approach Based on Conditional Probability Approximation</title><link>https://camerotachiara.github.io/papers/paper_el/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://camerotachiara.github.io/papers/paper_el/</guid><description>This paper investigates the generation of load profiles using a stochastic approach, focusing on creating robust estimations that rely solely on CPU resources. The methodology outlined emphasizes computational efficiency and is designed for practical implementation in resource-constrained environments. This work will be presented at the 2025 IEEE International Conference on Environment and Electrical Engineering and the 2025 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&amp;amp;CPS Europe), highlighting its relevance in the context of renewable energy management and optimization.</description></item></channel></rss>