Two-phase particle swarm optimized-support vector regression hybrid model integrated with improved empirical mode decomposition with adaptive noise for multiple-horizon electricity demand forecasting

AL‐Musaylh, Mohanad S.; Deo, Ravinesh C.; Li, Yan; Adamowski, Jan

doi:10.1016/j.apenergy.2018.02.140

Cited by 135 publications

(52 citation statements)

References 68 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Smaller values of RMSE reflect satellite-based precipitation estimates which are closer to the observations. RRMSE [5,[51][52][53] normalizes daily precipitation RMSE by the mean daily precipitation of the gauge stations. When RRMSE is more than 50%, such satellite-based precipitation products are considered unreliable.…”

Section: Discussionmentioning

confidence: 99%

Comprehensive Evaluation of GPM-IMERG, CMORPH, and TMPA Precipitation Products with Gauged Rainfall over Mainland China

Guang-hua

Lü

Crow

et al. 2018

Advances in Meteorology

View full text Add to dashboard Cite

over China. Moreover, IMERG V05B was compared with IMERG V04A, the Tropical Rainfall Measuring Mission (TRMM) 3B42, and the Climate Prediction Center Morphing technique (CMORPH)-CRT in this study. Categorical verification techniques and statistical methods are used to quantify their performance. Results illustrate the following.(1) Except for IMERG V04A's severe underestimation over the Tibetan Plateau (TP) and Xinjiang (XJ) with high negative relative biases (RBs) and CMORPH-CRT's overestimation over XJ with high positive RB, the four satellite-based precipitation products generally capture the same spatial patterns of precipitation over China.(2) At the annual scale over China, the IMERG products do not show an advantage over its predecessor (TRMM 3B42) in terms of RMSEs, RRMSEs, and Rs; meanwhile, the performance of IMERG products is worse than TRMM 3B42 in spring and summer according to the RMSE, RRMSE, and R metrics. Between the two IMERG products, IMERG V05B shows the anticipated improvement (over IMERG V04A) with a decrease in RMSE from 0.4496 to 0.4097 mm/day, a decrease of RRMSE from 16.95% to 15.44%, and an increase of R from 0.9689 to 0.9759 during the whole study period. Similar results are obtained at the seasonal scale. Among the four satellite products, CMORPH-CRTshows the worst seasonal performance with the highest RMSE (0.6247 mm/day), RRMSE (23.55%), and lowest R (0.9343) over China. (3) Over XJ and TP, IMERG V05B clearly improves the strong underestimation of precipitation in IMERG V04A with the RBs of 5.2% vs. −21.8% over XJ, and 2.78% vs. −46% over TP. Results at the annual scale are similar to those obtained at the seasonal scale, except for summer results over XJ. While, over the remaining subregions, the two IMERG products have a close performance; meanwhile, IMERG V04A slightly improves IMERG V05B's overestimation according to RBs (except for HN) at the annual scale. However, all four products are unreliable over XJ at both an annual and seasonal scale. (4) Across all products, TRMM 3B42 best reproduces the probability density function (PDF) of daily precipitation intensity. (5) According to the categorical verification technique in this study, both IMERG products yield better results for the detection of precipitation events on the basis of probability of detection (POD) and critical success index (CSI) categorical evaluations compared to TRMM 3B42 and CMORPH-CRT over China and across most of the subregions. However, all four products have room for further improvement, especially in high-latitude and dry climate regions. ese findings provide valuable feedback for both IMERG algorithm developers and data set users.

show abstract

Section: Discussionmentioning

confidence: 99%

Comprehensive Evaluation of GPM-IMERG, CMORPH, and TMPA Precipitation Products with Gauged Rainfall over Mainland China

Guang-hua

Lü

Crow

et al. 2018

Advances in Meteorology

View full text Add to dashboard Cite

show abstract

“…EPSs may use multiple fast-learning or statistical algorithms as classifier ensembles, e.g., ANNs, MLP, DTs, rotation forest (RF) bootstrap, and boosting, allowing higher accuracy and robustness. The subsequent ensemble prediction systems can be used to quantify the probability of floods, based on the prediction rate used in the event [142,143,144].…”

Section: Ensemble Prediction Systems (Epss)mentioning

confidence: 99%

“…Ouyang et al [145] and Zhang et al [146] presented a review of the applications of ensemble ML methods used for floods. EPSs were demonstrated to have the capability for improving model accuracy in flood modeling [140][141][142][143][144][145][146] To improve the accuracy of import data and to achieve better dataset management, the ensemble mean was proposed as a powerful approach coupled with ML methods [140,141]. Empirical mode decomposition (EMD) [142], and ensemble EMD (EEMD) [143] are widely used for flood prediction [144].…”

Section: Ensemble Prediction Systems (Epss)mentioning

confidence: 99%

Flood Prediction Using Machine Learning Models: Literature Review

Mosavi

Öztürk

Chau

2018

Water

976

469

View full text Add to dashboard Cite

Floods are among the most destructive natural disasters, which are highly complex to model. The research on the advancement of flood prediction models contributed to risk reduction, policy suggestion, minimization of the loss of human life, and reduction the property damage associated with floods. To mimic the complex mathematical expressions of physical processes of floods, during the past two decades, machine learning (ML) methods contributed highly in the advancement of prediction systems providing better performance and cost-effective solutions. Due to the vast benefits and potential of ML, its popularity dramatically increased among hydrologists.Researchers through introducing novel ML methods and hybridizing of the existing ones aim at discovering more accurate and efficient prediction models. The main contribution of this paper is to demonstrate the state of the art of ML models in flood prediction and to give insight into the most suitable models. In this paper, the literature where ML models were benchmarked through a qualitative analysis of robustness, accuracy, effectiveness, and speed are particularly investigated to provide an extensive overview on the various ML algorithms used in the field. The performance comparison of ML models presents an in-depth understanding of the different techniques within the framework of a comprehensive evaluation and discussion. As a result, this paper introduces the most promising prediction methods for both long-term and short-term floods. Furthermore, the major trends in improving the quality of the flood prediction models are investigated. Among them, hybridization, data decomposition, algorithm ensemble, and model optimization are reported as the most effective strategies for the improvement of ML methods. This survey can be used as a guideline for hydrologists as well as climate scientists in choosing the proper ML method according to the prediction task. Keywords: flood prediction; flood forecasting; flash-flood model, big flood management; hydrologic model; rainfall-runoff, hybrid & ensemble machine learning; artificial neural networks (ANNs); support vector machines (SVM); natural hazards & disasters; adaptive neuro-fuzzy inference system (ANFIS); decision trees (DT); internet of things (IoT); random forest (RF); survey; classification and regression trees (CART), data science; deep learning; big data; bagging, boosting, artificial intelligence (AI); soft computing; extreme event management; time series prediction; multilayer perceptron (MLP); simulated annealing (SA); multivariate adaptive regression splines (MARS), supervised learning IntroductionAmong the natural disasters, floods are the most destructive, causing massive damage to human life, infrastructure, agriculture, and the socioeconomic system.Governments, therefore, are under pressure to develop reliable and accurate maps of flood risk areas and further plan for sustainable flood risk management focusing on prevention, protection, and preparedness [1]. Flood prediction models are of significant impor...

show abstract

“…And then the results of all subtasks are accumulated as the final result. Based on this idea, a "decomposition and ensemble" framework was proposed and widely applied to the analysis of time series, such as energy forecasting [16,17], fault diagnosis [18][19][20], and biosignal analysis [21][22][23]. This framework consists of three stages.…”

Section: Introductionmentioning

confidence: 99%

A CEEMDAN and XGBOOST‐Based Approach to Forecast Crude Oil Prices

Zhou

Shi

et al. 2019

Complexity

View full text Add to dashboard Cite

Crude oil is one of the most important types of energy for the global economy, and hence it is very attractive to understand the movement of crude oil prices. However, the sequences of crude oil prices usually show some characteristics of nonstationarity and nonlinearity, making it very challenging for accurate forecasting crude oil prices. To cope with this issue, in this paper, we propose a novel approach that integrates complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and extreme gradient boosting (XGBOOST), so-called CEEMDAN-XGBOOST, for forecasting crude oil prices. Firstly, we use CEEMDAN to decompose the nonstationary and nonlinear sequences of crude oil prices into several intrinsic mode functions (IMFs) and one residue. Secondly, XGBOOST is used to predict each IMF and the residue individually. Finally, the corresponding prediction results of each IMF and the residue are aggregated as the final forecasting results. To demonstrate the performance of the proposed approach, we conduct extensive experiments on the West Texas Intermediate (WTI) crude oil prices. The experimental results show that the proposed CEEMDAN-XGBOOST outperforms some state-of-the-art models in terms of several evaluation metrics.

show abstract

Two-phase particle swarm optimized-support vector regression hybrid model integrated with improved empirical mode decomposition with adaptive noise for multiple-horizon electricity demand forecasting

Cited by 135 publications

References 68 publications

Comprehensive Evaluation of GPM-IMERG, CMORPH, and TMPA Precipitation Products with Gauged Rainfall over Mainland China

Comprehensive Evaluation of GPM-IMERG, CMORPH, and TMPA Precipitation Products with Gauged Rainfall over Mainland China

Flood Prediction Using Machine Learning Models: Literature Review

A CEEMDAN and XGBOOST‐Based Approach to Forecast Crude Oil Prices

Contact Info

Product

Resources

About