Electrical load forecasting in disaggregated levels using Fuzzy ARTMAP artificial neural network and noise removal by singular spectrum analysis

Müller, Marcel; Gaio, G.; Carreno, E. M.; Lotufo, Anna Diva P.; Teixeira, Luiz Albino

doi:10.1007/s42452-020-2988-5

Cited by 9 publications

(4 citation statements)

References 53 publications

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“…BPNN is the updated class of FFNN which contains an additional BP algorithm. Because of its accurate prediction, BP has also been used in RFNN [39], WNN [40], RNN [48], ENN [50], ART network [51], LSTM [61], and other techniques. Additionally, CNN is trained with BP and BP is a necessary part of CNN [146].…”

Section: Bp and Non-bp Based Load Forecastingmentioning

confidence: 99%

“…Such work would be extremely beneficial as there are not many works on mitigation of data centralization and computation load on central servers, problems which decentralization and distribution can help resolve. Some works have tried to demonstrate a reduction in training and computation time using limited database load forecasting [51], [115], [121], but the use of DML and DDL is still open for study. The current challenges and possible research scopes have shown in Table VIII.…”

Section: Current Research Scopesmentioning

confidence: 99%

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HSIC Bottleneck Based Distributed Deep Learning Model for Load Forecasting in Smart Grid With a Comprehensive Survey

et al. 2020

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Load forecasting is a vital part of smart grids for predicting the required electrical power using artificial intelligence (AI). Deep learning is broadly used for load forecasting in the smart grid using the artificial neural network (ANN). Generally, computing the deep learning in the smart grid requires massive data aggregation or centralization and significant computational time. This paper presents a survey of deep learning-based load forecasting techniques from 2015 to 2020. This survey discusses the studies based on their deep learning techniques, Distributed Deep Learning (DDL) techniques, Back Propagation (BP) based works, and non-BP based works in the load forecasting process. Consequent to the survey, it was determined that data aggregation dependency would be beneficial for reducing computational time in load forecasting. Therefore, a conceptual model of DDL for smart grids has been presented, where the HSIC (Hilbert-Schmidt Independence Criterion) Bottleneck technique has been incorporated to provide higher accuracy.

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Section: Bp and Non-bp Based Load Forecastingmentioning

confidence: 99%

Section: Current Research Scopesmentioning

confidence: 99%

HSIC Bottleneck Based Distributed Deep Learning Model for Load Forecasting in Smart Grid With a Comprehensive Survey

et al. 2020

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“…The importance of load data preprocessing for improving prediction accuracy is emphasized. Currently, commonly used preprocessing techniques include wavelet transform(WT) [18], empirical modal decomposition(EMD) [19], variational modal decomposition(VMD) [20], singular spectrum analysis [21], and more.…”

Section: Introductionmentioning

confidence: 99%

The Power Load Forecasting Model of Combined SaDE-ELM and FA-CAWOA-SVM Based on CSSA

Wang,

Ku,

Liu

2024

IEEE Access

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“…Khwaja et al designed integrated machine learning based on ANNs to improve short-term load forecasting by training bagged-boosted ANNs [18]. Müller et al obtained excellent results compared to traditional methods by using singular spectrum analysis (SSA) combined with fuzzy an adaptive resonance theory map (ARTMAP) ANN for noise removal [19]. Li et al proposed an improved short-term load forecasting method, the main component of which is the frequency decomposition of the load followed by different methods for forecasting different frequency components [20].…”

Section: Introductionmentioning

confidence: 99%

A novel decomposition-based ensemble broad learning system for short-term load forecasting

Tian

2024

AETR

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Load forecasting of the power system plays a key role in the production planning and actual operation scheduling of power systems. However, as the power system becomes larger and more complex, it is very important to propose a forecasting model with high accuracy and low computational cost. In this paper, a novel short-term load forecasting model is proposed, which combines the empirical wavelet transform (EWT) and the broad learning system (BLS). The advantage of EWT is that it can decompose the signal into multiple local frequency bands, select the local wavelet function adaptively, and overcome the modal mixing problem caused by the discontinuity of the signal time-frequency scale. While, the advantage of BLS is that it allows the model to learn more by stretching the width, and uses ridge regression to make it less time consuming and faster. To verify the effectiveness, the model is compared with some other state-of-the-art methods, and several performance estimations indicate that the model has high accuracy and low computational cost.

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Electrical load forecasting in disaggregated levels using Fuzzy ARTMAP artificial neural network and noise removal by singular spectrum analysis

Cited by 9 publications

References 53 publications

HSIC Bottleneck Based Distributed Deep Learning Model for Load Forecasting in Smart Grid With a Comprehensive Survey

HSIC Bottleneck Based Distributed Deep Learning Model for Load Forecasting in Smart Grid With a Comprehensive Survey

The Power Load Forecasting Model of Combined SaDE-ELM and FA-CAWOA-SVM Based on CSSA

A novel decomposition-based ensemble broad learning system for short-term load forecasting

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