A Decomposition-Based Improved Broad Learning System Model for Short-Term Load Forecasting

Cheng, Yuxin; Le, Haozhe; Li, Chunquan; Huang, Jiehui; Liu, Peter

doi:10.1007/s42835-022-01127-x

Cited by 3 publications

(3 citation statements)

References 45 publications

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“…In contrast to DNNs, the broad learning system (BLS), as an alternative method, enhances the generalization performance by expanding the width of a single hidden layer rather than improving its approximation capabilities through the extension of deep architectures (Feng et al, 2022). Some researchers utilize BLS as a method to enhance the accuracy of forecasting when dealing with large-scale datasets (Chen and Liu, 2017;Cheng et al, 2022;Zhu et al, 2022). The expanded broad structure of BLS ensures its strong approximation capability of nonlinear mapping (Wang et al, 2020) to ensure the accuracy of prediction results while significantly reducing computational costs, and the accuracy can be maintained using proper optimization techniques (Gong et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal prediction of photovoltaic power based on a broad learning system and an improved backtracking search optimization algorithm

Tang,

Huang,

Qian

et al. 2024

Front. Energy Res.

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The accuracy of photovoltaic (PV) power forecasting techniques relies not only on high-quality spatiotemporal data but also on an efficient feature-mining methodology. In this study, a spatiotemporal power forecasting model based on the broad learning system (BLS) and the improved backtracking search optimization algorithm (IBSOA) is proposed. The objective is to enhance the accuracy of PV power predictions while reducing the time-intensive training process associated with an extensive set of broad learning system parameters. The spatiotemporal attributes of historical data from multiple PV sites are clustered using a self-organizing map. The clustering analysis explores the spatiotemporal correlation among five photovoltaic (PV) power stations for each season between 2017 and 2018. Subsequently, the IBSOA is employed to optimize the hyperparameters of the BLS model, particularly the mapping and enhancement nodes. By utilizing hyperparameter optimization, a BSOA-based broad learning model is introduced to achieve superior accuracy. The results are assessed using the proposed method in comparison with three popular optimization algorithms: 1) genetic algorithm (GA), 2) bird swarm algorithm (BSA), and 3) backtracking search optimization algorithm (BSOA). All scenarios are validated and compared using PV plant data from the DKA center in Australia. The root-mean-square error (RMSE) indicators of the proposed prediction method are consistently lower than the worst-case scenario in each season, decreasing by 3.2283 kW in spring, 3.9159 kW in summer, 1.3425 kW in autumn, and 1.4058 kW in winter. Similarly, the mean absolute percentage error (MAPE) exhibits a reduction compared to the worst case, with a decreases of 0.882% in spring, 1.2399% in summer, 1.803% in autumn, and 1.087% in winter. The comprehensive results affirm that the proposed method surpasses alternative optimization techniques, delivering high-quality power forecasts for the given case study.

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Section: Introductionmentioning

confidence: 99%

Spatio-temporal prediction of photovoltaic power based on a broad learning system and an improved backtracking search optimization algorithm

Tang,

Huang,

Qian

et al. 2024

Front. Energy Res.

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“…This algorithm enables fast modeling and rapid model updates, making it applicable in various fields. Cheng Y and others proposed the D-BLS algorithm for load trend prediction [33]. Wang M and his team combined adaptive Kalman filtering with the BLS for battery-charging-state detection, achieving excellent detection results [34].…”

Section: Introductionmentioning

confidence: 99%

Wind Speed Modeling for Wind Farms Based on Deterministic Broad Learning System

Wang,

Xue

2023

Atmosphere

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As the penetration rate of wind power in the grid continues to increase, wind speed forecasting plays a crucial role in wind power generation systems. Wind speed prediction helps optimize the operation and management of wind power generation, enhancing efficiency and reliability. However, wind speed is a nonlinear and nonstationary system, and traditional statistical methods and classical intelligent algorithms struggle to cope with dynamically updating operating conditions based on sampled data. Therefore, from the perspective of optimizing intelligent algorithms, a wind speed prediction model for wind farms was researched. In this study, we propose the Deterministic Broad Learning System (DBLS) algorithm for wind farm wind speed prediction. It effectively addresses the issues of data saturation and local minima that often occur in continuous-time system modeling. To adapt to the continuous updating of sample data, we improve the sample input of the Broad Learning System (BLS) by using a fixed-width input. When new samples are added, an equivalent number of old samples is removed to maintain the same input width, ensuring the feature capture capability of the model. Additionally, we construct a dataset of wind speed samples from 10 wind farms in Gansu Province, China. Based on this dataset, we conducted comparative experiments between the DBLS and other algorithms such as Random Forest (RF), Support Vector Regression (SVR), Extreme Learning Machines (ELM), and BLS. The comparison analysis of different algorithms was conducted using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). Among them, the DBLS algorithm exhibited the best performance. The RMSE of the DBLS ranged from 0.762 m/s to 0.776 m/s, and the MAPE of the DBLS ranged from 0.138 to 0.149.

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“…To address the above problems, we propose an improved hybrid predictive model, which includes a sliding-window algorithm, a stacking ensemble neural network model, and a similar-days predictive method. Specifically, a sliding-window algorithm [26] is first introduced to directly process the nonlinearity and non-stationarity of the time-series electric load data. This method effectively mines spatiotemporal features of the time series.…”

mentioning

confidence: 99%

A Stacking Learning Model Based on Multiple Similar Days for Short-Term Load Forecasting

Jiang

Cheng

et al. 2022

Mathematics

Self Cite

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It is challenging to obtain accurate and efficient predictions in short-term load forecasting (STLF) systems due to the complexity and nonlinearity of the electric load signals. To address these problems, we propose a hybrid predictive model that includes a sliding-window algorithm, a stacking ensemble neural network, and a similar-days predictive method. First, we leverage a sliding-window algorithm to process the time-series electric load data with high nonlinearity and non-stationarity. Second, we propose an ensemble learning scheme of stacking neural networks to improve forecasting performance. Specifically, the stacking neural networks contain two types of networks: the base-layer and the meta-layer networks. During the pre-training process, the base-layer network integrates a radial basis function (RBF), random vector functional link (RVFL), and backpropagation neural network (BPNN) to provide a robust predictive model. The meta-layer network utilizes a deep belief network (DBN) and the improved broad learning system (BLS) to enhance predictive accuracy. Finally, the similar-days prediction method is developed to extract the relationship of electric load data in different time dimensions, further enhancing the robustness and accuracy of the model. To demonstrate the effectiveness of our model, it is evaluated using real data from five regions of the United States in three consecutive years. We compare our method with several state-of-the-art and conventional neural-network-based models. Our proposed algorithm improves the prediction accuracy by 16.08%, 16.83%, and 22.64% compared to DWT-EMD-RVFL, SWT-LSTM, and EMD-BLS, respectively. Empirical results demonstrate that our model achieves better accuracy and robustness compared with the baselines.

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A Decomposition-Based Improved Broad Learning System Model for Short-Term Load Forecasting

Cited by 3 publications

References 45 publications

Spatio-temporal prediction of photovoltaic power based on a broad learning system and an improved backtracking search optimization algorithm

Spatio-temporal prediction of photovoltaic power based on a broad learning system and an improved backtracking search optimization algorithm

Wind Speed Modeling for Wind Farms Based on Deterministic Broad Learning System

A Stacking Learning Model Based on Multiple Similar Days for Short-Term Load Forecasting

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