Short-term load forecasting of industrial customers based on SVMD and XGBoost

Wang, Yuanyuan; Sun, Shanfeng; Chen, Xiaoqiao; Zhang, Xiangjun; Kong, Yang; Chen, Jun; Guo, Yongsheng; Wang, Tingyuan

doi:10.1016/j.ijepes.2021.106830

Cited by 120 publications

(44 citation statements)

References 33 publications

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“…It is an ensemble model based on decision trees that combine multiple weak learners into strong learners through multiple iterative learning processes. It works by boosting numerous weak learners such as regression trees by assembling them to create a single but stronger learner [36]. The basic principle behind the process is to learn at each iteration sequentially, and the present regression tree is fitted with the residual from the previous three.…”

Section: B: Xgboost Regression Model and Hyperparameter Optimizationmentioning

confidence: 99%

“…where ŷXi denotes the predicted value of the i-th sample, M denotes the number of CART in the model, f m (x i ) represents the predicted value of the i-th sample in the mth tree, F is the function space of CART. The objective function of the XGBoost includes the MSE loss function and the regularisation term represented by (5) [36].…”

Section: B: Xgboost Regression Model and Hyperparameter Optimizationmentioning

confidence: 99%

“…If the model has a small value of eta, then it is more likely to overfit, but if the eta value is too large, the model is expected to underfit. It is now clear how significantly the choice XGBoost hyper-parameters improves its performance [36]. Determining the best hyper-parameters can be a painful task if one is required to perform them manually.…”

Section: B: Xgboost Regression Model and Hyperparameter Optimizationmentioning

confidence: 99%

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Data-Driven Predictive Maintenance of Wind Turbine Based on SCADA Data

Udo

Muhammad²

2021

IEEE Access

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Rystad Energy's analysis shows that installed offshore wind capacity will rise to 27.5 GW in 2026 from 10.5 GW in 2020. This report indicates that increasingly complex maintenance needs must be met for wind turbines(WTs). IRENA report shows that offshore wind operation and maintenance (OM) costs typically constitute 16-25% of the cost of electricity for offshore wind farms deployed in the G20 countries. Data collection and analytics, predictive maintenance, and production output optimisation of WTs must be explored to increase operational reliability and reduce maintenance costs of WTs. Predictive maintenance in wind turbines can be achieved by analysing data obtained by sensors already equipped with the WT. This network of sensors forms part of a Supervisory Control and Data Acquisition (SCADA) system. We developed a method for monitoring and detecting anomalies in the WT critical components, such as the gearbox and the generator. The proposed approach is based on the historical SCADA data that is common in most wind farms. We developed models using extreme gradient boosting (XGBoost) and Long Short-Term Memory (LSTM) to build the characteristics behaviour of critical WT components, and Statistical Process Control (SPC) was used to evaluate its anomalous behaviour. The proposed method was tested on two real case studies regarding six different WT to determine its effectiveness and applicability.

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Section: B: Xgboost Regression Model and Hyperparameter Optimizationmentioning

confidence: 99%

Section: B: Xgboost Regression Model and Hyperparameter Optimizationmentioning

confidence: 99%

Section: B: Xgboost Regression Model and Hyperparameter Optimizationmentioning

confidence: 99%

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Data-Driven Predictive Maintenance of Wind Turbine Based on SCADA Data

Udo

Muhammad²

2021

IEEE Access

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“…Similarly to Random Forest, XGBoost models are based on ensemble learning theory which combine gradient boosting decision trees models and second-order Taylor expansion on the loss function to speed up the optimisation process while avoiding overfitting [64]. XGBoost models also support parallel processing and therefore are faster to train and deploy than traditional decision trees.…”

Section: Machine Learning Modelsmentioning

confidence: 99%

Short and Very Short Term Firm-level Load Forecasting for Warehouses: A Comparison of Machine Learning and Deep Learning Models

Ribeiro¹,

Carmo²,

Endo³

et al. 2022

Preprint

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Commercial buildings are a significant consumer of energy worldwide. Logistics facilities, and specifically warehouses, are a common building type yet under-researched in the demand-side energy forecasting literature. Warehouses have an idiosyncratic profile when compared to other commercial and industrial buildings with a significant reliance on a small number of energy systems. As such, warehouse owners and operators are increasingly entering in to energy performance contracts with energy service companies (ESCOs) to minimise environmental impact, reduce costs, and improve competitiveness. ESCOs and warehouse owners and operators require accurate forecasts of their energy consumption so that precautionary and mitigation measures can be taken. This paper explores the performance of three machine learning models (Support Vector Regression (SVR), Random Forest, and Extreme Gradient Boosting (XGBoost)), three deep learning models (Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU)), and a classical time series model, Autoregressive Integrated Moving Average (ARIMA) for predicting daily energy consumption. The dataset comprises 8,040 records generated over an 11-month period from January to November 2020 from a non-refrigerated logistics facility located in Ireland. The grid search method was used to identify the best configurations for each model. The proposed XGBoost models outperform other models for both very short load forecasting (VSTLF) and short term load forecasting (STLF); the ARIMA model performed the worst.

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“…The Light Gradient Boosting Machine (LightGBM), XGBoost, Random forest, support vector regression (SVR), and Seasonal Autoregressive Integrated Moving Average (SARIMA) algorithm are used to build models. These methods have been widely used in recent studies [55][56][57][58][59]. The parameter optimization is conducted for each algorithm model for building the optimal models.…”

Section: Performance Measuresmentioning

confidence: 99%

A Hybridly Optimized LSTM-Based Data Flow Prediction Model for Dependable Online Ticketing

Fan

Zhu

Elahi

et al. 2021

Wireless Communications and Mobile Computing

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Fifth-generation (5G) communication technologies and artificial intelligence enable the design and deployment of sophisticated solutions for enhanced user experience and superior network-based service delivery. However, the performance of the systems offering 5G-based services depends on various factors. In this paper, we consider the case of the online railway ticketing system in China that serves the needs of hundreds of millions of people daily. This system’s online access rates vary over time, and fluctuations are experienced, affecting its overall dependability and service quality. We use long short-term memory network, particle swarm optimization, and differential evolution to construct DP-LSTM—a hybridly optimized model to predict network flow for dependable and quality-enhanced service delivery. We evaluate the proposed model using real data collected over six months from the “12306 online ticketing” system. We compare the performance of the proposed model with mainstream network traffic prediction models. We use mean absolute percentage error, mean absolute error, and root mean square error for performance evaluation. Experimental results show the superiority of the proposed model.

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Short-term load forecasting of industrial customers based on SVMD and XGBoost

Cited by 120 publications

References 33 publications

Data-Driven Predictive Maintenance of Wind Turbine Based on SCADA Data

Data-Driven Predictive Maintenance of Wind Turbine Based on SCADA Data

Short and Very Short Term Firm-level Load Forecasting for Warehouses: A Comparison of Machine Learning and Deep Learning Models

A Hybridly Optimized LSTM-Based Data Flow Prediction Model for Dependable Online Ticketing

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