Prediction of electricity consumption and hydropower production in the smart power grid based on the gated recurrent unit neural network and modified future search algorithm
Hao Tang, Yuening Wang, Xinping Yuan, Navid Razmjooy · 2025
Read Prediction of electricity consumption and hydropower production in the smart power grid based on the gated recurrent unit neural network and modified future search algorithm by Hao Tang, Yuening Wang, Xinping Yuan, Navid Razmjooy (2025) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-12748585.txt.
Description
Hao Tang and co-authors build a forecasting model for power use and water-based power output in a smart grid, in a 2025 paper. Good forecasts help grid operators share out energy well and waste less. The team pairs a neural network built for sequences, called a gated recurrent unit, with a tuned search method that adjusts the network's settings. Economic and social data feed the long-term forecasts.
Weather data drives the side of the model that predicts river-based power production. The authors set their tuned model against plain deep learning models and other tuned versions. Their results, they report, show better accuracy than the rivals they tested. Better forecasts of both demand and water power could help operators plan storage, schedule generation, and keep the grid steady as renewable sources grow.
Catalog record
| CREATOR | Hao Tang, Yuening Wang, Xinping Yuan, Navid Razmjooy |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 53 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |