ArticleScientific data2022
Solar and wind power data from the Chinese State Grid Renewable Energy Generation Forecasting Competition.
Article in Scientific data, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed.
- Halogen Bonding-Sustained Accumulation of Polyiodide in Hierarchically Open Micellar Film for Shuttle-Free Long-Duration Zn-IAngewandte Chemie (International ed. in English) · 2026Article
- MultiScaleWave: a wavelet-based multiscale framework for univariate time series forecasting.Scientific reports · 2026Article
- Fast-powerformer achieves accurate and memory-efficient mid-term wind power forecasting.Scientific reports · 2026Article
- FDMSNet: A frequency-decomposed multi-scale hybrid framework for renewable energy power forecasting.PloS one · 2026Article
- TCN-LSTM-AM Short-Term Photovoltaic Power Forecasting Model Based on Improved Feature Selection and APO.Sensors (Basel, Switzerland) · 2025Article
- A novel hybrid model integrating CEEMDAN decomposition, dispersion entropy and LSTM for photovoltaic power forecasting and anomaly detection.Scientific reports · 2025Article
- Fault Recovery Strategy with Net Load Forecasting Using Bayesian Optimized LSTM for Distribution Networks.Entropy (Basel, Switzerland) · 2025Article
- DTCMMA: Efficient Wind-Power Forecasting Based on Dimensional Transformation Combined with Multidimensional and Multiscale Convolutional Attention Mechanism.Sensors (Basel, Switzerland) · 2025Article
- A Golden Jackal based optimal operation and optimization of a virtual power plant involving renewable resources.Scientific reports · 2025Article
- Competitive evaluation and multi-stage planning of park integrated energy systems.Scientific reports · 2025Article
- Short-term power prediction of photovoltaic power stations based on Kepler optimization algorithm and VMD-CNN-LSTM model.PloS one · 2025Article
- Article
- Dung beetle optimization algorithm-based hybrid deep learning model for ultra-short-term PV power prediction.iScience · 2024Article
- Solar and wind power data from the Chinese State Grid Renewable Energy Generation Forecasting Competition.Scientific data · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Accurate solar and wind generation forecasting along with high renewable energy penetration in power grids throughout the world are crucial to the days-ahead power scheduling of energy systems. It is difficult to precisely forecast on-site power generation due to the intermittency and fluctuation characteristics of solar and wind energy. Solar and wind generation data from on-site sources are beneficial for the development of data-driven forecasting models. In this paper, an open dataset consisting of data collected from on-site renewable energy stations, including six wind farms and eight solar stations in China, is provided. Over two years (2019-2020), power generation and weather-related data were collected at 15-minute intervals. The dataset was used in the Renewable Energy Generation Forecasting Competition hosted by the Chinese State Grid in 2021. The process of data collection, data processing, and potential applications are described. The use of this dataset is promising for the development of data-driven forecasting models for renewable energy generation and the optimization of electricity demand response (DR) programs for the power grid.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.