An Ensemble Prediction System Based on Artificial Neural Networks and Deep Learning Methods for Deterministic and Probabilistic Carbon Price Forecasting

Yang, Yi; Guo, Honggang; Jin, Yu; Song, Aiyi

doi:10.3389/fenvs.2021.740093

Cited by 19 publications

(8 citation statements)

References 40 publications

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“…This part further verifies the performance of the IMODA by executing four classical test functions (ZDT1‐ZDT4 (Li et al, 2019)), and their expressions are listed in the Appendix . IMODA is compared with three other classical swarm intelligence optimization algorithms, including original multi‐objective Dragonfly optimization algorithm (MODA) (Yang, Guo, et al, 2021); multi‐objective grasshopper optimization algorithm (MOGOA) (Mirjalili et al, 2018), and multi‐objective particle swarm optimization algorithm (MOPSO) (Tripathi et al, 2007). In addition, multi‐objective performance evaluation is divided into convergence index and diversity distribution index.…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, considering the predicted performance and actual calculation time of the developed model, 50 or 100 iterations are considered as the optimal number of iterations. (Yang, Guo, et al, 2021); multi-objective grasshopper optimization algorithm (MOGOA) (Mirjalili et al, 2018), and multi-objective particle swarm optimization algorithm (MOPSO) (Tripathi et al, 2007). In addition, multi-objective performance evaluation is divided into convergence index and diversity distribution index.…”

Section: Scenarios 1: Sensitivity Analysismentioning

confidence: 99%

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Photovoltaic power combination prediction system based on improved multi‐objective optimization algorithm and nonlinear weighting strategy

2022

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The large-scale connection of photovoltaic (PV) power generation to the power grid introduces considerable challenges to the grid's stable operation. To ensure grid stability, the key is to improve the accuracy of PV power prediction. Currently, PV power prediction is primarily dependent on single-model construction, which ignores the role of data processing technologies, thus resulting in inadequate prediction performance. Accordingly, in this study, a combined prediction system is developed by integrating advanced deep learning algorithms and data processing techniques to improve the accuracy of PV power prediction. In addition, a nonlinear weighting strategy based on an improved multi-objective dragonfly optimization algorithm (IMODA) is proposed to determine the final prediction result. In the IMODA, cloud model generator, tent mapping, and Pareto solution selection strategy based on knee points are introduced to resolve the defects of the original algorithm. The performance of the proposed combined system was scientifically evaluated and analysed by considering the PV power datasets of four seasons in Belgium as an example. The mean absolute percentage errors of the proposed system on the four datasets are 4. 0198, 4.7943, 4.3587, and 5.9286. These values indicate an improvement range of 5%-30% compared with the errors of other benchmark models.

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Section: Discussionmentioning

confidence: 99%

Section: Scenarios 1: Sensitivity Analysismentioning

confidence: 99%

Photovoltaic power combination prediction system based on improved multi‐objective optimization algorithm and nonlinear weighting strategy

2022

Self Cite

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“…Ensemble Prediction Systems (EPSs) face several major problems, such as improving Numerical Weather Predictions (NWPs) and understanding the overall source of errors and biases in the system. Other issues associated with EPSs arise from the complexity associated with complex data integration techniques and not having enough case studies for validation [143]. Putting EPSs into operational settings and telling end users about uncertainty and probabilistic forecasts in a clear way are other challenges that EPSs face.…”

Section: Ensemble Predictionsmentioning

confidence: 99%

Evolution of Flood Prediction and Forecasting Models for Flood Early Warning Systems: A Scoping Review

Byaruhanga,

Kibirige,

Gokool

et al. 2024

Water

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Floods are recognised as one of the most destructive and costliest natural disasters in the world, which impact the lives and livelihoods of millions of people. To tackle the risks associated with flood disasters, there is a need to think beyond structural interventions for flood protection and move to more non-structural ones, such as flood early warning systems (FEWSs). Firstly, this study aimed to uncover how flood forecasting models in the FEWSs have evolved over the past three decades, 1993 to 2023, and to identify challenges and unearth opportunities to assist in model selection for flood prediction. Secondly, the study aimed to assist in model selection and, in return, point to the data and other modelling components required to develop an operational flood early warning system with a focus on data-scarce regions. The scoping literature review (SLR) was carried out through a standardised procedure known as Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The SLR was conducted using the electronic databases Scopus and Web of Science (WoS) from 1993 until 2023. The results of the SLR found that between 1993 and 2010, time series models (TSMs) were the most dominant models in flood prediction and machine learning (ML) models, mostly artificial neural networks (ANNs), have been the most dominant models from 2011 to present. Additionally, the study found that coupling hydrological, hydraulic, and artificial neural networks (ANN) is the most used ensemble for flooding forecasting in FEWSs due to superior accuracy and ability to bring out uncertainties in the system. The study recognised that there is a challenge of ungauged and poorly gauged rainfall stations in developing countries. This leads to data-scarce situations where ML algorithms like ANNs are required to predict floods. On the other hand, there are opportunities to use Satellite Precipitation Products (SPP) to replace missing or poorly gauged rainfall stations. Finally, the study recommended that interdisciplinary, institutional, and multisectoral collaborations be embraced to bridge this gap so that knowledge is shared for a faster-paced advancement of flood early warning systems.

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“…On the other hand, considering that the single forecasting model has certain limitations and cannot accurately capture the practical characteristics of the carbon price series, the existing carbon price research gradually adopts the combination forecasting method [16]. This method may fully exploit the benefits of each individual prediction model, hence increasing forecast accuracy in particular.…”

Section: Introductionmentioning

confidence: 99%

Carbon Price Combination Forecasting Model Based on Lasso Regression and Optimal Integration

Yang

Jia-ming

et al. 2023

Sustainability

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Accurate carbon price index prediction can delve deeply into the internal law of carbon price changes, provide helpful information to managers and decision makers, as well as improve the carbon market system. Nevertheless, existing methods for combination forecasting typically arbitrarily choose a certain set of single forecasting models. However, a particular selection of forecasting models do not apply to all data sets due to the nonlinearity and nonsmoothness of the carbon trading price series. Therefore, choosing suitable single forecasting models for the combination is crucial. Considering the limitations of the current study, this study constructs a combined carbon trading forecasting model based on Lasso regression and optimal integration. By invoking the Lasso regression model, we can select suitable single forecasting models for combination forecasting based on the variation patterns of different training sets. Meanwhile, ARIMA, NARNN, LSTM, and 11 other single forecasting models are screened in this study, including both traditional statistical forecasting models and artificial intelligence forecasting models. First, the carbon price index is predicted using 11 single prediction models. Furthermore, given the multi-collinearity of the single prediction series, this study employs Lasso regression to reduce the dimensions of the single prediction models, which are then used to construct an optimal combination prediction model. Finally, the proposed model is applied to SZA-2017 and SZA-2019 carbon price data in Shenzhen. The results demonstrate that the model developed in this study outperforms other benchmark prediction models in terms of prediction error and direction accuracy, showing the efficacy of the proposed method.

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An Ensemble Prediction System Based on Artificial Neural Networks and Deep Learning Methods for Deterministic and Probabilistic Carbon Price Forecasting

Cited by 19 publications

References 40 publications

Photovoltaic power combination prediction system based on improved multi‐objective optimization algorithm and nonlinear weighting strategy

Photovoltaic power combination prediction system based on improved multi‐objective optimization algorithm and nonlinear weighting strategy

Evolution of Flood Prediction and Forecasting Models for Flood Early Warning Systems: A Scoping Review

Carbon Price Combination Forecasting Model Based on Lasso Regression and Optimal Integration

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