LSTM Based Short-term Electricity Consumption Forecast with Daily Load Profile Sequences

Kim, Nakyoung; Kim, Minkyung; Choi, Jun Kyun

doi:10.1109/gcce.2018.8574484

Cited by 37 publications

(13 citation statements)

References 6 publications

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“…This paper proposes a deep learning NN with one LSTM layer and two fully connected layers for the prediction of BGL using CGM data. Predicting glucose using LSTM Nns is promising [8] since LSTM NNs were successfully applied in other domains such as prediction of water quality [10], electricity consumption [11] and stock prices [12].…”

mentioning

confidence: 99%

Predicting blood glucose using an LSTM Neural Network

Idriss¹,

Idri

Abnane

et al. 2019

Proceedings of the 2019 Federated Conference on Computer Science and Information Systems

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Diabetes self-management relies on the blood glucose prediction as it allows taking suitable actions to prevent low or high blood glucose level. In this paper, we propose a deep learning neural network (NN) model for blood glucose prediction. It is a sequential one using a Long-Short-Term Memory (LSTM) layer with two fully connected layers. Several experiments were carried out over data of 10 diabetic patients to decide on the model's parameters in order to identify the best variant of it. The performance of the proposed LSTM NN measured in terms of root mean square error (RMSE) was compared with the ones of an existing LSTM and an autoregressive (AR) models. The results show that our LSTM NN is significantly more accurate; in fact, it outperforms the existing LSTM model for all patients and outperforms the AR model in 9 over 10 patients, besides, the performance differences were assessed by the Wilcoxon statistical test. Furthermore, the mean of the RMSE of our model was 12.38 mg/dl while it was 28.84 mg/dl and 50.69 mg/dl for AR and the existing LSTM respectively.

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mentioning

confidence: 99%

Predicting blood glucose using an LSTM Neural Network

Idriss¹,

Idri

Abnane

et al. 2019

Proceedings of the 2019 Federated Conference on Computer Science and Information Systems

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show abstract

“…The results of the experiments in Sections 3.2 and 3.3 are summarized as Table II, which shows that the proposed LSTM method with attention mechanism has the highest prediction 0.98 ARIMA [9] 0.53 SVM [11] 0.69 Neural Network (3 hidden layers) 0.70 Neural Network (4 hidden layers) 0.72 Neural Network (5 hidden layers) 0.71 LSTM [14] (without attention) 0.91 LSTM (with attention, proposed) 0.99 accuracy. Prediction accuracy increased by 6.5% compared to state-of-the-art model (LSTM without attention mechanism).…”

Section: Comparative Experimentsmentioning

confidence: 99%

“…Among them, Long Short‐Term Memory (LSTM) and Gated Recurrent Unit (GRU) are two different variants of recurrent neural networks (RNNs), which have better predictive effects on time series prediction. The method is proposed to utilize the LSTM network, which takes a sequence of past consumption profiles to perform a month‐ahead electricity consumption prediction as a sequence . The multilayer GRU is used to construct the model to predict the electricity consumption .…”

Section: Introductionmentioning

confidence: 99%

Electricity consumption prediction based on LSTM with attention mechanism

Lin

Cheng

Huang

2020

IEEJ Transactions Elec Engng

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Power data analysis in power system, such as electricity consumption prediction, has always been the basis for the power department to adjust electricity price, substation regulation, total load prediction and peak avoidance management. In this paper, a short‐term time‐phased electricity consumption prediction model based on Long Short‐Term Memory (LSTM) with an attention mechanism is proposed. First, the attention mechanism is used to assign weight coefficients to the input sequence data. Then, the output value of every cell of LSTM is calculated according to the forward propagation method, and the error between the real value and the predicted value is calculated using the back‐propagation method. The gradient of each weight is calculated according to the corresponding error term, and the weight of the model is updated by the gradient descent direction to make the error smaller. Using modeling and predicting experiments on different types of electricity consumption, the results show that the prediction accuracy of the model proposed increased by 6.5% compared to the state‐of‐the‐art model. The model has a good effect on electricity consumption prediction. Not only can it be close to actual results numerically, but it can also better predict the development trend of data. © 2020 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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“…Kim et al [20] proposed a short-term electricity consumption prediction method. The LSTM network is used to predict month-ahead electricity consumption.…”

Section: Related Workmentioning

confidence: 99%

Predicting Low-Cost Gas Sensor Readings From Transients Using Long Short-Term Memory Neural Networks

Gambiroza

Mastelić

Kovačević

et al. 2020

IEEE Internet Things J.

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With the everyday growth of the Internet of Things (IoT), the number of connected sensor devices increases as well, where each sensor consumes energy while being constantly online. During that time, they collect large amounts of data in short intervals leading to the collection of redundant and perhaps irrelevant data. Moreover, being commonly battery powered, sensor batteries need to be frequently replaced or recharged. The former requires smarter and less frequent data collection, while the latter being complementary to the former requires putting them to sleep while not being used in order to save energy. The focus of this article is low-cost gas sensors as they need to preheat for several minutes to reliably collect gas concentration. However, instead of waiting for a sensor to heat up, a transient, i.e., a data trend that the sensor collects while heating up is analyzed. It is shown that long short-term memory (LSTM) neural network can be used to learn and later predict the actual gas level from a part of the transient. This way, instead of being constantly online or fully preheating, the sensor needs to be turned on for only 20 s and then sleep for 120 s. With high accuracy, our approach decreases energy consumption by up to 85% compared to a system where sensors are constantly online, and more than 50% compared to a system where a sensor collects actual values instead of a part of the transient.

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LSTM Based Short-term Electricity Consumption Forecast with Daily Load Profile Sequences

Cited by 37 publications

References 6 publications

Predicting blood glucose using an LSTM Neural Network

Predicting blood glucose using an LSTM Neural Network

Electricity consumption prediction based on LSTM with attention mechanism

Predicting Low-Cost Gas Sensor Readings From Transients Using Long Short-Term Memory Neural Networks

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