Integrating data decomposition and machine learning methods: An empirical proposition and analysis for renewable energy generation forecasting. (15th October 2022)

• Record Type: Journal Article
• Authors: Ding, Song; Zhang, Huahan; Tao, Zui; Li, Ruojin
• Is Part Of: Expert systems with applications. Volume 204(2022)
• Publication Date: 2022-10-15
• Subjects: Long-short term memory -- Seasonal-trend decomposition procedure based on loess -- Ensemble framework -- Renewable energy generation -- Grid flexibility enhancement; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2022.117635
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: An ensemble data-driven STL-LSTM forecasting framework is proposed. This proposed model is used for renewable energy generation forecasting. The new model can grasp the nonlinear, trend, and periodic patterns in datasets. The proposed method strikingly outperforms a range of prevalent benchmarks. Abstract: Renewable energy generation (REG) has been vigorously developed under the dual pressure of environmental contamination and fossil fuel energy shortage. Whereas the rapidly increasing REG instigates stochastic volatility may threaten the REG deployment and grid stability. Motivated by the grid flexibility enhancement depending on the expansion of grid infrastructure that belongs to the mid-term management domain, the precise predictions for mid-term, especially monthly REG sourced from diverse renewable energy, are indispensable for power plant configuration, electricity end-uses promotion, and grid expansion to propel the energy system transformation. To this end, aiming at diverse monthly REG datasets characterized by periodicity, nonlinearity, and volatility, the seasonal-trend decomposition procedure based on loess ( STL ) is initially employed to extract the trend and periodic features of monthly REG datasets, and generate trend, seasonal, and remainder subseries. Based on the decomposed data, long-short term memory ( LSTM ) is utilized for subseries prediction, and then the projections are integrated to compose the ultimate forecasted results for original REG observations. To validate the efficacy and adaptability of the proposed data-driven STL-LSTM framework, several machine learning methods and autoregressive models are involved in predicting practical monthly electricity generation datasets sourced by solar, wind, hydropower, and geothermal energy in different countries. The forecasted results indicate that the proposed framework is demonstrated with superlative prediction performance and strong adaptability for generation prediction derived from diverse renewable energy. Therefore, the novel STL-LSTM framework may constitute a promising alternative for REG forecasting.