Randomised learning‐based hybrid ensemble model for probabilistic forecasting of PV power generation

Liu, Wei; Xu, Yan

doi:10.1049/iet-gtd.2020.0625

Cited by 26 publications

(9 citation statements)

References 32 publications

(33 reference statements)

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“…Zhao et al (2020) combined the results of ENN and GRNN with a definite integral to achieve interval forecasting, and obtained a large number of high-precision results of the annual storm surge disaster economic losses. At present, ensemble models are widely proposed to reduce bias and to improve forecasting accuracy (Liu and Xu, 2020;Bravo and Ayuso, 2021), but the application of this model in the field of marine disaster forecasting remains rare (Ding et al, 2020). Zhao et al (2019) proposed an ensemble learning model called Adaboost-BPNN which is designed to forecast direct economic losses of marine disasters.…”

Section: Literature Reviewmentioning

confidence: 99%

Modeling of Direct Economic Losses of Storm Surge Disasters Based on a Novel Hybrid Forecasting System

2022

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Modeling the direct economic losses of storm surge disasters can assess the disaster situation in a timely manner and improve the efficiency of post-disaster management in practice, which is acknowledged as one of the most significant issues in clean production. However, improving the forecasting accuracy of direct economic losses caused by storm surge disasters remains challenging, which is also a major concern in the field of disaster risk management. In particular, most of the previous studies have mainly focused on individual models, which ignored the significance of reduction and optimization. Therefore, a novel direct economic loss forecasting system for storm surge disasters is proposed in this study, which includes reduction, forecasting, and evaluation modules. In this system, a forecasting module based on an improved machine learning technique is proposed, which improves the generalization ability and robustness of the system. In addition, the key attributes and samples are selected by the proposed reduction module to further improve the forecasting performance from the two innovative perspectives. Moreover, an evaluation module is incorporated to comprehensively evaluate the superiority of the developed forecasting system. Data on the storm surge disasters from three typical provinces are utilized to conduct a case study, and the performance of the proposed forecasting system is analyzed and compared with eight comparison models. The experimental results show that the mean absolute percentage error (MAPE) predicted by the Extreme Learning Machine (ELM) model was 16.5293%, and the MAPE predicted by the proposed system was 1.0313%. Overall, the results show that the performance of the proposed forecasting system is superior compared to other models, and it is suitable for the forecasting of direct economic losses resulting from storm surge disasters.

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Section: Literature Reviewmentioning

confidence: 99%

Modeling of Direct Economic Losses of Storm Surge Disasters Based on a Novel Hybrid Forecasting System

2022

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“…Liu and Xu (2020) proposed a randomised learning-based hybrid ensemble (RLHE) model to construct the prediction intervals of probabilistic solar power output forecasting [51]. Chai et al (2019) modelled a time learning weight to improve the time correlation of the LSTM network for PV output power prediction [52].…”

Section: Plos Onementioning

confidence: 99%

Variational mode decomposition combined fuzzy—Twin support vector machine model with deep learning for solar photovoltaic power forecasting

et al. 2022

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A novel Variational Mode Decomposition (VMD) combined Fuzzy-Twin Support Vector Machine Model with deep learning mechanism is devised in this research study to forecast the solar Photovoltaic (PV) output power in day ahead basis. The raw data from the solar PV farms are highly fluctuating and to extract the useful stable components VMD is employed. A novel Fuzzy–Twin Support Vector Machine (FTSVM) model developed acts as the forecasting model for predicting the solar PV output power for the considered solar farms. The twin support vector machine (SVM) model formulates two separating hyperplanes for predicting the output power and in this research study a fuzzy based membership function identifies most suitable two SVM prediction hyperplanes handling the uncertainties of solar farm data. For the developed, new VMD-FTSVM prediction technique, their optimal parameters for the training process are evaluated with the classic Ant Lion Optimizer (ALO) algorithm. The solar PV output power is predicted using the novel VMD-FTSVM model and during the process multi-kernel functions are utilized to devise the two fuzzy based hyperplanes that accurately performs the prediction operation. Deep learning (DL) based training of the FTSVM model is adopted so that the deep auto-encoder and decoder module enhances the accuracy rate. The proposed combined forecasting model, VMD-ALO-DLFTSVM is validated for superiority based on a two 250MW PV solar farm in India. Results prove that the proposed model outperforms the existing model in terms of the performance metrics evaluated and the forecasted PV Power.

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“…The above studies mainly focus on the improvement and optimization of point prediction methods, which are characterized by the availability of corresponding deterministic information, but cannot quantify the impact of multiple uncertainties on CETP trend forecasting [48]. In contrast, probabilistic interval forecast can describe the probability of occurrence of forecast outcomes and can provide decision makers with more information, which is now widely used in areas such as renewable energy output forecasting [49][50][51][52].…”

Section: Introductionmentioning

confidence: 99%

Multi‐objective expansion planning of park‐level integrated energy system considering the volatility trend of CETP

Wang

Dai

Liu-zhu³

et al. 2022

IET Generation Trans & Dist

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With the rapid development of carbon trading market, the volatility trend of carbon emission trading price (CETP) becomes one of the factors that cannot be ignored in energy system planning. Based on this, this paper proposes a multi-objective expansion planning model for park-level integrated energy system (PIES) that takes into account the volatility trend of CETP. First, the influencing factors of CETP prediction are filtered and downscaled, and a kernel extreme learning machine (KELM) model based on the improved multi-objective grey wolf algorithm optimizer (IMOGWO) is used for probabilistic interval prediction of CETP. Next, the operational characteristics of each carbon emission device are analysed and a model for calculating the cost of carbon trading is proposed. Then, a two-layer PIES planning model is developed with the objective of minimizing the annualized system cost and carbon emissions during the expansion planning cycle, the upper-layer model is a planning model for solving equipment expansion scenarios, and the lower-layer model is an operation model for calculating typical operation schemes. Finally, the simulation effect of the prediction model is verified by the European carbon trading data, and the planning schemes are compared and analysed to prove the effectiveness of the proposed method.

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Randomised learning‐based hybrid ensemble model for probabilistic forecasting of PV power generation

Cited by 26 publications

References 32 publications

Modeling of Direct Economic Losses of Storm Surge Disasters Based on a Novel Hybrid Forecasting System

Modeling of Direct Economic Losses of Storm Surge Disasters Based on a Novel Hybrid Forecasting System

Variational mode decomposition combined fuzzy—Twin support vector machine model with deep learning for solar photovoltaic power forecasting

Multi‐objective expansion planning of park‐level integrated energy system considering the volatility trend of CETP

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