Short-Term Load Forecasting Based on Frequency Domain Decomposition and Deep Learning

Zhang, Qian; Ma, Yi; Li, Guoli; Ma, Jie; Ding, Jinjin

doi:10.1155/2020/7240320

Cited by 13 publications

(13 citation statements)

References 23 publications

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“…In this paper, by applying the limitation on factors according to (3) and ( 4), the possibility of the dynamic characteristic control on the particle swarms Mathematical Problems in Engineering and making a balance between local search and global search is provided [100]. According to (3), the factors related to PSO algorithm are considered as in (4).…”

Section: Particle Swarm Optimization (Pso) Algorithmmentioning

confidence: 99%

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Short-Term Load Forecasting Using Neural Network and Particle Swarm Optimization (PSO) Algorithm

Chafi

Afrakhte

2021

Mathematical Problems in Engineering

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Electrical load forecasting plays a key role in power system planning and operation procedures. So far, a variety of techniques have been employed for electrical load forecasting. Meanwhile, neural-network-based methods led to fewer prediction errors due to their ability to adapt properly to the consuming load's hidden characteristic. Therefore, these methods were widely accepted by the researchers. As the parameters of the neural network have a significant impact on its performance, in this paper, a short-term electrical load forecasting method using neural network and particle swarm optimization (PSO) algorithm is proposed, in which some neural network parameters including learning rate and number of hidden layers are determined in order to forecast electrical load using the PSO algorithm precisely. Then, the neural network with these optimized parameters is used to predict the short-term electrical load. In this method, a three-layer feedforward neural network trained by backpropagation algorithm is used beside an improved gbest PSO algorithm. Also, the neural network prediction error is defined as the PSO algorithm cost function. The proposed approach has been tested on the Iranian power grid using MATLAB software. The average of three indices beside graphical results has been considered to evaluate the performance of the proposed method. The simulation results reflect the capabilities of the proposed method in accurately predicting the electrical load.

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Section: Particle Swarm Optimization (Pso) Algorithmmentioning

confidence: 99%

“…Due to the great ability in nonlinear relationships modelling between inputs and outputs, artificial neural networks are increasingly used in load forecasting [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Short-Term Load Forecasting Using Neural Network and Particle Swarm Optimization (PSO) Algorithm

Chafi

Afrakhte

2021

Mathematical Problems in Engineering

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“…which attain competitive advantages for nonlinear load mapping and generalisation, although offering hinderances to criteria making and parameter setting. With the advancements in technology and computer aided predictions, the process of power load forecasting has seen improvements via ANN, wavelet transforms, fuzzy algorithms, SVM etc [8][9][10][11].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Owing to improved capabilities of nonlinear relationship modelling between the inputs and outputs, ANNs are quite often employed to forecast load [8][9][10][11]. The ANN method for short term load forecasting, operates by making computers mimic the mathematical model of a human brain [4].…”

Section: Literature Reviewmentioning

confidence: 99%

Micro-Genetic Algorithm Embedded Multi-Population Differential Evolution based Neural Network for Short-Term Load Forecasting

Joy

Pillai

Chen

et al. 2021

2021 56th International Universities Power Engineering Conference (UPEC)

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The load of a power system usually presents a certain range of nonlinear fluctuation with time. Even then, the load characteristics still follow certain rules which can be exploited to optimise and improve the accuracy of computerbased Short-Term Load Forecasting (STLF) models. Therefore, this paper presents a mGA (micro-Genetic Algorithm) embedded multi-population DE (Differential Evolution) to optimise an Artificial Neural Network (ANN) STLF model. Firstly, the mGA embedded multi-population DE is proposed, to improve and balance the global and local search. Then the proposed DE is applied to optimise the weights during the training of the ANN. The overall model's performance is evaluated using publicly available Panama electricity load dataset against four state-of-the-art machine learning algorithms. The evaluation results show that the proposed DE based NN STLF model has higher prediction accuracy compared to the other selected machine learning algorithms.

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“…Singlelayer Perceptron (SLP), which has no hidden layer, was used for increasing SLTF speed in a hybrid SLP with Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU), Encoder-Decoder Architecture, and Auto-encoder (AE) [98]. RNN is a directed graph-oriented ANN and hybrid RNN is broadly used in various load forecasting processes for data cleaning [59], big data [71], computational time, high dimensional data [133], and other purposes. LSTM and GRU are techniques built upon RNN.…”

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

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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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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Short-Term Load Forecasting Based on Frequency Domain Decomposition and Deep Learning

Cited by 13 publications

References 23 publications

Short-Term Load Forecasting Using Neural Network and Particle Swarm Optimization (PSO) Algorithm

Short-Term Load Forecasting Using Neural Network and Particle Swarm Optimization (PSO) Algorithm

Micro-Genetic Algorithm Embedded Multi-Population Differential Evolution based Neural Network for Short-Term Load Forecasting

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

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