Bagging–XGBoost algorithm based extreme weather identification and short-term load forecasting model

Deng, Xing; Ye, Aoshuang; Zhong, Jiashi; Dong, Xu; Yang, Wangwang; Song, Zuwei; Zhang, Zitong; Guo, J.; Wang, Tao; Tian, Yufei; Pan, Hongguang; Zhang, Zhijing; Wang, Hui; Chen, Wu; Shao, Jihai; Chen, Xiaoyi

doi:10.1016/j.egyr.2022.06.072

Cited by 46 publications

(4 citation statements)

References 28 publications

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“…At the same time, weighted similarity selection is substituted to improve RT load forecasting accuracy of the model. 40 Li et al constructed a RT sequence forecasting model based on Xgboost to make accurate RT forecasting for the instability of crustal plate movement. 41 Compared with conventional statistical RT forecasting methods, advantage of RT forecasting methods based on machine learning is that it can deal with both linear and nonlinear time series.…”

Section: Development Of Rt Load Forecastingmentioning

confidence: 99%

“…Deng et al used bagging and Xgboost to analyze the relationship between short term load data and weather influencing factors. At the same time, weighted similarity selection is substituted to improve RT load forecasting accuracy of the model 40 . Li et al constructed a RT sequence forecasting model based on Xgboost to make accurate RT forecasting for the instability of crustal plate movement 41 …”

Section: Related Workmentioning

confidence: 99%

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A spatio‐temporal graph convolutional approach to real‐time load forecasting in an edge‐enabled distributed Internet of Smart Grids energy system

Liu,

Pan,

Cao

et al. 2024

Concurrency and Computation

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SummaryAs the edge nodes of the Internet of Smart Grids (IoSG), smart sockets enable all kinds of power load data to be analyzed at the edge, which create conditions for edge calculation and real‐time (RT) load forecasting. In this article, an edge‐cloud computing analysis energy system is proposed to collect and analyze power load data, and a combination of graph convolutional network (GCN) with LSTM, called KGLSTM is used to achieve mid‐long term mixed sequential mode RT forecasting. In the proposed edge‐cloud framework, distributed intelligent sockets are regarded as edge nodes to collect, analyze and upload data to cloud services for further processing. The proposed KGLSTM network adopts a double branch structure. One branch extracts the data characteristics of mid‐short term time‐series data through an encoding–decoding LSTM module; the other branch extracts the data features of long term timing data through an adapted GCN. GCN is used to extract spatial correlations between different nodes. In addition, by combining a dynamic weighted loss function, the accuracy of peak forecasting is effectively improved. Finally, through various experimental indicators, this article shows that KGLSTM and weighted KGLSTM have achieved significant performance improvement over recent methods in mid‐long term time‐series forecasting and peak forecasting.

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Section: Development Of Rt Load Forecastingmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A spatio‐temporal graph convolutional approach to real‐time load forecasting in an edge‐enabled distributed Internet of Smart Grids energy system

Liu,

Pan,

Cao

et al. 2024

Concurrency and Computation

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show abstract

“…Fang [23] conducted a study on forecasting the price of foreclosed houses in a Chinese province using BP neural network and optimized the parameters of the BP model using GA algorithm in the modeling process. Recently, integrated learning models have been widely used in various fields because of their unique learning approach [24][25][26]. In the house price prediction problem, Zhu and Li [27] utilized the gradient boosting decision tree (GBDT) model to forecast the price of second-hand houses in China and the particle swarm optimization (PSO) algorithm to determine the model's hyperparameters.…”

Section: Introductionmentioning

confidence: 99%

A Study on the Prediction of House Price Index in First-Tier Cities in China Based on Heterogeneous Integrated Learning Model

Mao

Duan

Guo

et al. 2022

Journal of Mathematics

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To address the difficulty of low prediction accuracy, insufficient model stability, and certain lag associated with a single machine learning model in the prediction of house price, this paper proposes a multimodel fusion house price prediction model based on stacking integrated learning. Firstly, web search data affecting house prices were collected by web crawler technology, and Spearman correlation analysis was performed on the attribute set to reduce its complexity and establish a prediction index system for four first-tier cities in China. Secondly, with the goal of improving accuracy, diversity, and generalization ability, the types of base learners as well as metalearners are determined, and the parameters of the base learners are optimized using the grey wolf optimization algorithm to produce the GWO-stacking model, and the experimental results from four datasets demonstrate that the model has high prediction accuracy. Finally, to solve the issue of unintelligible black boxes in machine learning models, we have used the state-of-the-art interpretation method SHAP combined with the LightGBM algorithm to interpret the model, and the result can be used as a basis for real estate policy planning and adjustment and even guide the demand of home buyers, thus improving the efficiency and effectiveness of government policy making.

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“…Deng etc. proposes a Bagging-XGBoost algorithm for short-term load forecasting model, which can warn the time period and detailed value of peak load of distribution transformer 5 . Chen etc.…”

Section: Introductionmentioning

confidence: 99%

Optimised extreme gradient boosting model for short term electric load demand forecasting of regional grid system

Qinghe

Wen

Huang

et al. 2022

Sci Rep

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Load forecast provides effective and reliable guidance for power construction and grid operation. It is essential for the power utility to forecast the exact in-future coming energy demand. Advanced machine learning methods can support competently for load forecasting, and extreme gradient boosting is an algorithm with great research potential. But there is less research about the energy time series itself as only an internal variable, especially for feature engineering of time univariate. And the machine learning tuning is another issue to applicate boosting method in energy demand, which has more significant effects than improving the core of the model. We take the extreme gradient boosting algorithm as the original model and combine the Tree-structured Parzen Estimator method to design the TPE-XGBoost model for completing the high-performance single-lag power load forecasting task. We resample the power load data of the Île-de-France Region Grid provided by Réseau de Transport d’Électricité in the day, train and optimise the TPE-XGBoost model by samples from 2016 to 2018, and test and evaluate in samples of 2019. The optimal window width of the time series data is determined in this study through Discrete Fourier Transform and Pearson Correlation Coefficient Methods, and five additional date features are introduced to complete feature engineering. By 500 iterations, TPE optimisation ensures nine hyperparameters’ values of XGBoost and improves the models obviously. In the dataset of 2019, the TPE-XGBoost model we designed has an excellent performance of MAE = 166.020 and MAPE = 2.61%. Compared with the original model, the two metrics are respectively improved by 14.23 and 14.14%; compared with the other eight machine learning algorithms, the model performs with the best metrics as well.

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Bagging–XGBoost algorithm based extreme weather identification and short-term load forecasting model

Cited by 46 publications

References 28 publications

A spatio‐temporal graph convolutional approach to real‐time load forecasting in an edge‐enabled distributed Internet of Smart Grids energy system

A spatio‐temporal graph convolutional approach to real‐time load forecasting in an edge‐enabled distributed Internet of Smart Grids energy system

A Study on the Prediction of House Price Index in First-Tier Cities in China Based on Heterogeneous Integrated Learning Model

Optimised extreme gradient boosting model for short term electric load demand forecasting of regional grid system

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