Work

An Efficient Data Cleaning Method for Renewable Energy Power Stations Integrating Anomaly Detection and Feature Enhancement

Year: 2026

Type: article

Abstract: Improving the prediction accuracy of renewable energy power generation units is an important goal of the “source-storage integration” approach. However, the abundance of anomalous data and indistinct features in renewable energy station data seriously affects the health status prediction of these generator sets. To effectively enhance the performance of renewable energy generation prediction, this paper proposes an efficient data cleaning method for renewable energy stations based on anomaly detection and feature enhancement. First, anomaly detection is achieved by calculating a baseline power curve and partitioning data, utilizing the Density-Based Spatial Clustering of Applications with Noise (DBSCAN). Subsequently, considering that current models often learn low-frequency features while ignoring high-frequency features when processing time-series data, a data feature enhancement method is proposed. The proposed method integrates high-/low-frequency data decomposition, time–frequency domain conversion, and an improved attention mechanism to effectively enhance the high-frequency features of renewable energy station data, and reduces the RMSE of mainstream forecasting models significantly. Finally, using data from a renewable energy station in a region of China, the effectiveness and superiority of the anomaly detection and feature enhancement methods are analyzed. The results show that for renewable energy generation data, the proposed method reduces the RMSE of LSTM and Transformer models by 15.12%, 16.67% and 16.24%, 18.32% respectively, significantly improving prediction accuracy.

Source: Energies

DOI: 10.3390/en19092075

Authors: Zifen Han, Chunxiang Yang, Fuwen Wang, Peipei Yang, Zongyang Liu, Wen Tang

Institutions: Xidian University, State Grid Corporation of China (China)

Language: English

Topic: Energy Load and Power Forecasting

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure, Climate action

Open Access status: gold

Estimated APC paid: $3,137