Development of chaotically improved meta-heuristics and modified BP neural network-based model for electrical energy demand prediction in smart grid

Islam, Badar ul; Baharudin, Z.; Nallagownden, Perumal

doi:10.1007/s00521-016-2408-3

Cited by 36 publications

(14 citation statements)

References 32 publications

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“…Multi Layer Perceptron (MLP) ANN (23) ( [? ], [14], [25], [42], [45], [78], [101], [110], [122], [124], [132], [156], [164], [171], [178], [187], [210], [211], [213], [228], [237], [242], [256]), Support Vector Machines (SVM) (21) ( [? ], [36], [53], [57], [65], [78], [79], [106], [115], [117], [122], [157], [159], [166], [187], [193], [203], [227], [240], [253], [256]), autoregressive integrated moving average (ARIMA) (13) ( [6], [19], [32], [42], [53], [78], …”

Section: Sms Resultsmentioning

confidence: 99%

“…Power loads / consumption analysis consumption clustering ( [109], [136], [137], [182], [190], [198], [199], [206], [210], [221], [259], [263]), consumption prediction ( [? ], [6], [10]- [12], [19], [23], [24], [32], [36], [42], [45], [53], [57], [65], [69], [70], [78], [79], [86], [88], [90], [98], [101]- [103], [106], [110], [111], [115]- [117], [122], [124], [125], [132], [146], [152], [156]- [159], [161], [162]…”

Section: B Rq2mentioning

confidence: 99%

See 1 more Smart Citation

Smart Grids Data Analysis: A Systematic Mapping Study

Rossi

Chren

2020

IEEE Trans. Ind. Inf.

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Article under review. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.The article is structured as follows. In section II, we provide an overview of Smart Grids concepts: mainly architecture and components of the SG infrastructure. In section III, we define the goals, needs, process and research questions for the Systematic Mapping Study on data analysis in the SG context. In section IV, we present the results from the overall SMS process, divided into answers to the main research questions set. In Section V, we discuss the main threats to validity of the current study. Section VI provides the final conclusions.

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Section: Sms Resultsmentioning

confidence: 99%

Section: B Rq2mentioning

confidence: 99%

Smart Grids Data Analysis: A Systematic Mapping Study

Rossi

Chren

2020

IEEE Trans. Ind. Inf.

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“…Model based on BP Neural Network BP (Backpropagation) neural network is a kind of multi-layer feedforward network trained by the error backpropagation algorithm, and it is one of the most widely used neural network models at present. [14] When describing the price change of the stock, only the historical data of the capital, that is, 13 indicators, are taken into account. When predicting the price change of the stock, the exchange rate, the exchange rate change, and the stock index are comprehensively considered.…”

Section: Establishment Of Regression Predictionmentioning

confidence: 99%

Predicting stock price based on BP neural network model

2020

JME

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The increase in the stock will affect the development of the economy, so the description and forecast of the capital are fundamental. Therefore, this paper first analyzes the historical data of Everbright Bank stock (601818) and explains the stock increase, the share affected by the exchange rate, and the index correlation analysis. Then a prediction model based on BP neural network is established. BP is used to train stock descriptors to predict stock prices for the first 30 trading days in early 2019. Combined with ARE and MSE for error analysis, the average relative error is 3.6%, and the mean-variance is 0.041, indicating that the prediction effect of the model is good. To make a specific contribution to the study of stock price prediction.

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“…However, the algorithm easily falls into the local minima trap and the training process is extremely slow. To solve this problem, a momentum term is introduced to improve the convergence rate (Islam et al, 2017).…”

Section: Predictive Control Based On the Mtn Modelmentioning

confidence: 99%

Nonlinear system predictive control using a multi-dimensional Taylor network

Zhang

2019

Transactions of the Institute of Measurement and Control

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A predictive control scheme on the basis of multi-dimensional Taylor network (MTN), named as MTN predictive compensation control, is proposed for single-input single-output nonlinear systems in this paper. We consider the MTN model as a one-step-ahead predictive model and train it by back-propagation (BP) algorithm with a momentum term, and then control the system by the predictive control law. Furthermore, to improve the anti-disturbance performance of the system, another MTN model is considered as the compensator and trained by a recursive least-squares algorithm to counteract the disturbance. An example is used to verify the effectiveness of the proposed scheme, in which the noise disturbance is considered. For comparison, the scheme that combines a radial basis function (RBF) neural network with a proportional-integral-derivative (PID) controller (RBF-PID), the predictive control scheme that combines a predictive neural network with a control neural network based on a BP neural network, and MTN predictive control are introduced. In addition, the difference in computational complexity between the MTN predictive compensation control scheme and the RBF-PID scheme is also considered. The experimental results show that the proposed scheme is effective, has good anti-disturbance performance in predictive control for the nonlinear system and is superior to the other comparison schemes.

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Development of chaotically improved meta-heuristics and modified BP neural network-based model for electrical energy demand prediction in smart grid

Cited by 36 publications

References 32 publications

Smart Grids Data Analysis: A Systematic Mapping Study

Smart Grids Data Analysis: A Systematic Mapping Study

Predicting stock price based on BP neural network model

Nonlinear system predictive control using a multi-dimensional Taylor network

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