Adaboost-Based Power System Load Forecasting

Ling, WuNeng; Zhang, Pan; Li, QiuWen; Dong, Ming; Luo, Changqing; Li, Yiming; Liang, Zhencheng

doi:10.1088/1742-6596/2005/1/012190

Cited by 3 publications

(2 citation statements)

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“…At present, Bagging, AdaBoost, XGBoost, GBDT and other integrated learning algorithms [46] are widely used. Bagging adopts parallel mode, and the training speed is fast, but some data sets can not be obtained due to back sampling, and each learner has the same weight, without considering its importance AdaBoost runs in serial mode and can adjust the weight according to the algorithm training results.…”

Section: Integrated Learning Algorithmmentioning

confidence: 99%

The Research of Short-term Electric Load Forecasting based on Machine Learning Algorithm

Wang¹,

Sun²,

Cai³

2023

AETR

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The ability of electric load forecasting become the key to measuring electric planning and dispatching, and improving the accuracy of electric load forecasting has become one hot spot topic of scholars in recent years. The traditional electric load forecasting algorithm mainly include statistical learning algorithm. The electric load forecasting algorithm based on traditional forecasting algorithm, machine learning algorithm and neural network algorithm greatly improve the convergence approximation effect and accuracy. In recent years, the electric load forecasting algorithms based on the deep learning algorithm has greatly reduced the error and improved the measurement accuracy and robustness, such as RNN, GRU, LSTM, DBN, TCN, and so on, as well as the combination algorithm based on deep learning. This paper introduces the classical algorithm of deep learning in electric load forecasting. The main purpose is to explore the error and fitting degree of algorithms established by different algorithms, and provide reference for the selection of forecasting algorithms for various types of electric load forecasting.

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Section: Integrated Learning Algorithmmentioning

confidence: 99%

The Research of Short-term Electric Load Forecasting based on Machine Learning Algorithm

Wang¹,

Sun²,

Cai³

2023

AETR

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“…Therefore, the increasingly widespread adoption of DERs necessitates relatively detailed load analysis and predictions. For the load forecasting of power systems, for decades, local and international researchers have used various calculation methods and forecasting models to continuously improve the accuracy of prediction [12][13][14][15]. In this study, an improved B-P algorithm is used to forecast the annual load of an industrial park and construct the corresponding model to prepare for subsequent user segmentation and distribution network optimization.…”

Section: Theories and Models 21 Load Forecasting Modelsmentioning

confidence: 99%

Distribution Network Optimization Model of Industrial Park with Distributed Energy Resources under the Carbon Neutral Targets

Yu,

Yang

2023

ENERGY

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Taking an industrial park as an example, this study aims to analyze the characteristics of a distribution network that incorporates distributed energy resources (DERs). The study begins by summarizing the key features of a distribution network with DERs based on recent power usage data. To predict and analyze the load growth of the industrial park, an improved back-propagation algorithm is employed. Furthermore, the study classifies users within the industrial park according to their specific power consumption and supply requirements. This user segmentation allows for the introduction of three constraints: node voltage, wire current, and capacity of DERs. By incorporating these constraints, the study constructs an optimization model for the distribution network in the industrial park, with the objective of minimizing the total operation and maintenance cost. The primary goal of these optimizations is to address the needs of DERs connected to the distribution network, while simultaneously mitigating their potential adverse impact on the network. Additionally, the study aims to enhance the overall energy efficiency of the industrial park through more efficient utilization of resources.

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Mid- to Long-Term Electric Load Forecasting Based on the EMD–Isomap–Adaboost Model

Han

Hong

et al. 2022

Sustainability

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Accurate load forecasting is an important issue for the reliable and efficient operation of a power system. In this study, a hybrid algorithm (EMDIA) that combines empirical mode decomposition (EMD), isometric mapping (Isomap), and Adaboost to construct a prediction mode for mid- to long-term load forecasting is developed. Based on full consideration of the meteorological and economic factors affecting the power load trend, the EMD method is used to decompose the load and its influencing factors into multiple intrinsic mode functions (IMF) and residuals. Through correlation analysis, the power load is divided into fluctuation term and trend term. Then, the key influencing factors of feature sequences are extracted by Isomap to eliminate the correlations and redundancy of the original multidimensional sequences and reduce the dimension of model input. Eventually, the Adaboost prediction method is adopted to realize the prediction of the electrical load. In comparison with the RF, LSTM, GRU, BP, and single Adaboost method, the prediction obtained by this proposed model has higher accuracy in the mean absolute percentage error (MAPE), mean absolute error (MAE), root mean square error (RMSE), and determination coefficient (R2). Compared with the single Adaboost algorithm, the EMDIA reduces MAE by 11.58, MAPE by 0.13%, and RMSE by 49.93 and increases R2 by 0.04.

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Adaboost-Based Power System Load Forecasting

Cited by 3 publications

References 0 publications

The Research of Short-term Electric Load Forecasting based on Machine Learning Algorithm

The Research of Short-term Electric Load Forecasting based on Machine Learning Algorithm

Distribution Network Optimization Model of Industrial Park with Distributed Energy Resources under the Carbon Neutral Targets

Mid- to Long-Term Electric Load Forecasting Based on the EMD–Isomap–Adaboost Model

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