Summary of Ai-powered Predictions For Electricity Load in Prosumer Communities, by Aleksei Kychkin et al.
AI-Powered Predictions for Electricity Load in Prosumer Communities
by Aleksei Kychkin, Georgios C. Chasparis
First submitted to arxiv on: 21 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores how short-term demand response mechanisms can optimize electricity consumption in communities with residential buildings, including those with renewable energy sources and energy storage (prosumers). The study focuses on the importance of accurate electricity load forecasts for each building and community. It reviews various forecasting techniques, including AI-powered models such as Facebook’s Prophet, LSTM models, SARIMA models, Holt-Winters models, and empirical regression-based models that utilize domain knowledge. The integration of weather forecasts into data-driven time series forecasts is also tested. Results show that a combination of persistent and regression terms achieves the best forecast accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how homes with solar panels and energy storage can work together to use less energy at times when it’s most needed. To make this happen, we need to be able to predict exactly how much energy each home will need in real-time. This requires using special tools called forecasting models. The researchers tested different types of forecasting models to see which ones worked best. They found that combining several techniques produced the most accurate predictions. This is important because it can help reduce energy waste and make our energy system more efficient. |
Keywords
* Artificial intelligence * Lstm * Regression * Time series
