Hybrid Model Combined Fuzzy Multi-Objective Decision Making with Feed Forward Neural Network (F-MODM-FFNN) For Very Short-Term Load Forecasting Based on Weather Data

Yundra, Eppy; Kartini, Unit Three; Wardani, Laili; Ardianto, Dwi

doi:10.22266/ijies2020.0831.16

Cited by 4 publications

(4 citation statements)

References 25 publications

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“…RNN is the basis upon which LSTM and GRU are built, and vice versa. The long short-term memory (LSTM) technique is the most widely utilised artificial neural network (ANN) method for load forecasting [17]. This shows that it provides a superior presentation in a wide variety of challenging settings.…”

Section: B Various Ann Methods In Deep Learning Supported Load Foreca...mentioning

confidence: 99%

An exposition on the prediction of load on a Smart Grid

Singh¹,

K²,

Gujjula³

2023

IJSMEM

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Smart grids depend on AI-based load forecasting to estimate future power demand (AI). Deep learning is especially important in smart grid load forecasting with neural networks (ANN). Processing time and data are needed to count smart grid deep learning. Combining data would speed load projections. The bottleneck strategy has been abandoned to attain this precision. Keeping the lights on requires short-term electricity demand prediction. But, the load’s intricacy and volatility make it fun to predict. EEMD breaks the load into many frequency-dependent components of different strengths. MLR predicts low-frequency regularities, while LSTM neural networks predict high-frequency components. Computational extent is unchanged. Despite its varied aggregation scope, the electric grid’s large data can be used to create the most effective deep learning models for Short-term Load Forecasting (STLF) in electrical networks. Hence, a suitable forecasting strategy uses deep learning with a Micro-clustering (MC) job that mixes unsupervised and supervised clustering tasks utilizingKmeans and Gaussian Support Vector Machine. To guarantee accuracy. B-bidirectional LSTMs can store feed-forward and future hidden-layer data. Feedback and feed-forward loops do this. The DaviesBouldering index determined cluster production per hour. MC with B-LSTM networks improves prediction,especially around spike locations. Forecasting RE generation and grid load is difficult. Prosumer microgrids (PMGs) sell electricity to aggregators. A hybrid machine learning-based load and weather data transmission method provides the biggest benefit. ANFIS, MLP, and radial basis function artificial neural networks (ANNs) would be used in this technique (RBF). Machine learning-based hybrid forecasting can improve accuracy.

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Section: B Various Ann Methods In Deep Learning Supported Load Foreca...mentioning

confidence: 99%

An exposition on the prediction of load on a Smart Grid

Singh¹,

K²,

Gujjula³

2023

IJSMEM

View full text Add to dashboard Cite

show abstract

“…So, it would be beneficial to reduce the computation times for load forecasting in the smart grid. Due to the disadvantages of BP, non-BP based works have been proposed for load forecasting [44] [46] [49]. In other application areas, alternative approaches to BP are also being investigated [28] [151] [152] [153].…”

Section: Deep Learning Process Contains Artificialmentioning

confidence: 99%

“…Table II displays some relevant works from 2015 to 2020 related to deep learning-based load forecasting in smart grids. [44], Particle Swarm Optimization [100], Copula [123], and other machine learning mechanisms in various load forecasting models. KNN-ANN is the combination of K-nearest Neighbor (KNN) and ANN which is used with FFNN in the hydro-thermal unit generation-based VSTLF process [75].…”

Section: B Different Ann Techniques In Deep Learning Based Load Forementioning

confidence: 99%

“…In a few cases, MI-ANN, WNN, GRU, DBN, RBM, ANFIS, and ART network approaches were applied to obtain better performances. Cascade NN, KNN-ANN [44], [48], [63], [65], [66], [73], [74], [75], [96], [109], [124], [129], [131], [133], [134], [137], [ [40], [42], [45] - [65], [67] - [73], [76] - [95], [97], [98], [101] - [103], [105] - [107], [110] - [119], [121], [123] - [126], [130] - [41], [42], [45] - [48], [52], [57], [61] - [63], [65], [69], [70], [72], [76],…”

Section: B Different Ann Techniques In Deep Learning Based Load Forementioning

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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