Effective Adam-Optimized LSTM Neural Network for Electricity Price Forecasting

Chang, Zihan; Zhang, Yang; Chen, Wenbo

doi:10.1109/icsess.2018.8663710

Cited by 69 publications

(37 citation statements)

References 2 publications

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“…Mean accuracy MIoU FCN with Adam (10) 0.9775 0.9660 0.9403 FCN with AdaGrad (9) 0.9768 0.9654 0.9386 FCN with SGD (7) 0.9782 0.9685 0.9423 DeepLabv2 (5) 0.9713 0.9663 0.9389 Proposed method 0.9824 0.9816 0.9577 voting method, the fuzzy integral is used for the fusion of multiple FCNs with various optimal methods. Experimental results show that FCN-8s has a better performance than FCN-16s and FCN-32s.…”

Section: Accuracy Overall Accuracymentioning

confidence: 99%

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Using Fully Convolutional Networks for Floor Area Detection

Lin¹,

Li²,

Lee³

2020

Sensors and Materials

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Most mobile robots use visual images to obtain information about the surrounding environment and the nonlinear diffusion method to detect candidate areas of the floor, but they could not be applied to more complicated environments. In this study, a hybrid of fully convolutional networks (FCNs) and fuzzy integral is proposed for detecting the position of the floor and nonfloor from visual images. FCN is an end-to-end, pixels-to-pixels network for semantic segmentation. Semantic segmentation aims to perform dense segmentation tasks on images and segments each pixel to a specified category. To overcome the majority decision drawback in the traditional voting method and increase the accuracy, the fuzzy integral is used for the fusion of multiple FCNs with various optimal methods. The overall accuracy, mean accuracy, and mean intersection over union (MIoU) of the proposed method are 0.9824, 0.9816, and 0.9577, respectively. The experimental results show that the proposed hybrid method has better accuracy than other methods in identifying the location of the floor area.

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Section: Accuracy Overall Accuracymentioning

confidence: 99%

“…When the gradient approaches zero, it will cause the training to end early. Adam (10) adopts an adaptive learning rate for each parameter. After the deviation correction is performed, each parameter is calculated for each iteration.…”

Section: Introductionmentioning

confidence: 99%

Using Fully Convolutional Networks for Floor Area Detection

Lin¹,

Li²,

Lee³

2020

Sensors and Materials

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“…N is the total number of samples in the testing set. The Adam optimizer (shown in Algorithm 2), an adaptive learning rate optimization algorithm [52], is employed to train the model.…”

Section: Hybrid Schemementioning

confidence: 99%

Convolution and Long Short-Term Memory Hybrid Deep Neural Networks for Remaining Useful Life Prognostics

et al. 2019

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Reliable prediction of remaining useful life (RUL) plays an indispensable role in prognostics and health management (PHM) by reason of the increasing safety requirements of industrial equipment. Meanwhile, data-driven methods in RUL prognostics have attracted widespread interest. Deep learning as a promising data-driven method has been developed to predict RUL due to its ability to deal with abundant complex data. In this paper, a novel scheme based on a health indicator (HI) and a hybrid deep neural network (DNN) model is proposed to predict RUL by analyzing equipment degradation. Explicitly, HI obtained by polynomial regression is combined with a convolutional neural network (CNN) and long short-term memory (LSTM) neural network to extract spatial and temporal features for efficacious prognostics. More specifically, valid data selected from the raw sensor data are transformed into a one-dimensional HI at first. Next, both the preselected data and HI are sequentially fed into the CNN layer and LSTM layer in order to extract high-level spatial features and long-term temporal dependency features. Furthermore, a fully connected neural network is employed to achieve a regression model of RUL prognostics. Lastly, validated with the aid of numerical and graphic results by an equipment RUL dataset from the Commercial Modular Aero-Propulsion System Simulation(C-MAPSS), the proposed scheme turns out to be superior to four existing models regarding accuracy and effectiveness.

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“…Long short-term memory (LSTM), which is a type of a recurrent neural network, shows a good performance in several fields such as language modelling and speech recognition [14]. LSTM is also successfully used in projection of energy consumption.…”

Section: Introductionmentioning

confidence: 99%

Effects of COVID-19 on electric energy consumption in Turkey and ANN-based short-term forecasting

Özbay¹,

Dalcalı²

2021

Turk J Elec Eng & Comp Sci

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Due to the coronavirus, millions of people worldwide carry out their work, education, shopping, culture, and entertainment activities from their homes using the advantages of today's technology now. Apart from this, patient care and follow-up are carried out with the help of electronic equipment especially in the institutions where health services are provided. It is important for humanity to perform all these services by providing a reliable electricity supply. In this study, outlook of the energy in Turkey were examined. The current energy consumption and investments were examined. Then, the precautions by the government against the pandemic period according to the occurrence and spread of COVID-19 in the country are given in chronological order. The actual electricity consumption data were obtained daily across the country, after all these precautions. It was observed that electricity consumption decreased significantly, especially on restricted days. It is inevitable that energy consumption estimation should be made in the short term so that the energy sector is not adversely affected by this situation. In this study, a more accurate short-term consumption forecasting methods were developed during the COVID-19 pandemic period, non-linear autoregressive (NARX) and long short term memory (LSTM) artificial neural networks. Between January-April 2019 electrical consumption data were used to train and validate the forecast prediction. The NARX and LSTM is a potential candidate for effective forecasting of electricity energy consumption. However, the obtained LSTM results suggest that the proposed method performed better results than NARX ANN.

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Effective Adam-Optimized LSTM Neural Network for Electricity Price Forecasting

Cited by 69 publications

References 2 publications

Using Fully Convolutional Networks for Floor Area Detection

Using Fully Convolutional Networks for Floor Area Detection

Convolution and Long Short-Term Memory Hybrid Deep Neural Networks for Remaining Useful Life Prognostics

Effects of COVID-19 on electric energy consumption in Turkey and ANN-based short-term forecasting

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