Summary of Clustering-based Multitasking Deep Neural Network For Solar Photovoltaics Power Generation Prediction, by Hui Song et al.
Clustering-based Multitasking Deep Neural Network for Solar Photovoltaics Power Generation Prediction
by Hui Song, Zheng Miao, Ali Babalhavaeji, Saman Mehrnia, Mahdi Jalili, Xinghuo Yu
First submitted to arxiv on: 9 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed CM-DNN framework for PV power generation prediction addresses the uncertainty of energy scheduling in smart grids. By applying K-means clustering to categorize data into different customer types, the model learns patterns specific to each group. For each type, a DNN is trained until accuracy can’t be improved, and inter-model knowledge transfer is conducted to enhance training accuracy. The framework is tested on a real-world PV power generation dataset, demonstrating its superiority over a single model without clustering. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Predicting solar energy production is important for managing our energy supply. However, predicting this energy depends on many factors, like where the sun shines and who uses the energy. In reality, we don’t have much information about these users, making it hard to create accurate predictions. To solve this problem, scientists developed a new approach using machine learning. They grouped similar users together and created a model for each group. This way, the model can learn specific patterns for each type of user. The team tested their idea on real-world data and found that it worked much better than previous methods. |
Keywords
» Artificial intelligence » Clustering » K means » Machine learning
