A novel hybrid load forecasting framework with intelligent feature engineering and optimization algorithm in smart grid. (1st October 2021)

• Record Type: Journal Article
• Authors: Hafeez, Ghulam; Khan, Imran; Jan, Sadaqat; Shah, Ibrar Ali; Khan, Farrukh Aslam; Derhab, Abdelouahid
• Is Part Of: Applied energy. Volume 299(2021)
• Publication Date: 2021-10-01
• Subjects: Electric load forecasting -- Feature engineering -- Modified fire-fly optimization algorithm -- Support vector regression -- Smart grid; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2021.117178
• Languages: English
• ISSNs: 0306-2619

Abstract

Abstract: Real-time, accurate, and stable forecasting plays a vital role in making strategic decisions in the smart grid (SG). This ensures economic savings, effective planning, and reliable and secure power system operation. However, accurate and stable forecasting is challenging due to the uncertain and intermittent electric load behavior. In this context, a rigid forecasting model with assertive stochastic and non-linear behavior capturing abilities is needed. Thus, a support vector regression (SVR) model emerged to cater the non-linear time-series predictions. However, it suffers from computational complexity and hard-to-tune appropriate parameters problem. Due to these problems, forecasting results of SVR are not as accurate as required. To solve such problems, a novel hybrid approach is developed by integrating feature engineering (FE) and modified fire-fly optimization (mFFO) algorithm with SVR, namely FE-SVR-mFFO forecasting framework. FE eliminates redundant and irrelevant features to ensure high computational efficiency. The mFFO algorithm obtains and tunes the SVR model's appropriate parameters to effectively avoid trapping into local optimum and returns accurate forecasting results. Besides, most literature studies are focused on forecast accuracy improvement. However, the forecasting model's effectiveness and productiveness are determined equally by its stability and convergence rate. Considering only one objective (accuracy or stability or convergence rate) is inadequate; thus, the proposed FE-SVR-mFFO forecasting framework achieves these three relatively independent objectives simultaneously. To evaluate the effectiveness and applicability of the proposed framework, real half-hourly load data of five states of Australia (New South Wales (NSW), Queensland (QLD), South Australia (SA), Tasmania (TAS), and Victoria (VIC)) are employed as a case study. Experimental results show that the proposed framework outperforms benchmark frameworks like EMD-SVR-PSO, FS-TSFE-CBSSO, VMD-FFT-IOSVR, and DCP-SVM-WO in terms of accuracy, stability, and convergence rate. Highlights: A novel FE-SVR-mFFO model is proposed for electric load forecasting. Feature engineering method is used to speed up the SVR model training process. A modified firefly algorithm is introduced to select and optimize SVR parameters. Prediction results confirm the proposed model's applicability in aspects of objectives compared to conventional models. The model is generic and can be applied to a variety of indicators and geographic regions.