Using Deep Learning Techniques to Forecast Environmental Consumption Level

Lee, Donghyun; Kang, Suna; Shin, Jungwoo

doi:10.3390/su9101894

Cited by 34 publications

(20 citation statements)

References 37 publications

(46 reference statements)

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“…These results show that the forecast values of the LSTM model are nearly approached to the true values, thus the proposed model could be appropriate for sensor data with time series, and it could be applied to the real world. These results also consist with other works in this domain [5], [7], [9], [11].…”

Section: Resultssupporting

confidence: 91%

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Deep Learning Approach for Forecasting Water Quality in IoT Systems

Nguyen¹,

Thanh-Hai²,

Ngôn³

2020

IJACSA

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Global climate change and water pollution effects have caused many problems to the farmers in fish/shrimp raising, for example, the shrimps/fishes had early died before harvest. How to monitor and manage quality of the water to help the farmers tackling this problem is very necessary. Water quality monitoring is important when developing IoT systems, especially for aquaculture and fisheries. By monitoring the real-time sensor data indicators (such as indicators of salinity, temperature, pH, and dissolved oxygen-DO) and forecasting them to get early warning, we can manage the quality of the water, thus collecting both quality and quantity in shrimp/fish raising. In this work, we introduce an architecture with a forecasting model for the IoT systems to monitor water quality in aquaculture and fisheries. Since these indicators are collected every day, they becomes sequential/time series data, we propose to use deep learning with Long-Short Term Memory (LSTM) algorithm for forecasting these indicators. Experimental results on several data sets show that the proposed approach works well and can be applied for the real systems.

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Section: Resultssupporting

confidence: 91%

“…The authors in [11] used deep-learning technologies in the environmental field to predict the status of pro-environmental consumption. The authors predicted the pro-environmental consumption index based on Google search query data, using a recurrent neural network (RNN) model.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Approach for Forecasting Water Quality in IoT Systems

Nguyen¹,

Thanh-Hai²,

Ngôn³

2020

IJACSA

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“…Ahmad et al forecasted energy demand by constructing a deep NN and inputting the information of weather and building usage rate [17]. Lee et al estimated environmental consumption by using a temporal model like recurrent neural network (RNN) with energy consumption data and temporal features [18]. Li et al proposed a method to predict energy demand with autoencoder, one of the methods to represent data [19].…”

Section: Related Workmentioning

confidence: 99%

“…In the variational autoencoder (VAE), the state defined on the latent space contains the feature of the produced data, and also contains the information of the expected energy consumption, as well as features of the input values [30,31]. Ma and Lee predicted the energy consumption by adding more information of the surrounding environment while learning [15,18]. However, unlike them, after learning to predict the consumption with only demand to date, our model can predict future consumption by adjusting the state on the latent space with the condition of the surrounding environment.…”

Section: Overviewmentioning

confidence: 99%

Electric Energy Consumption Prediction by Deep Learning with State Explainable Autoencoder

2019

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As energy demand grows globally, the energy management system (EMS) is becoming increasingly important. Energy prediction is an essential component in the first step to create a management plan in EMS. Conventional energy prediction models focus on prediction performance, but in order to build an efficient system, it is necessary to predict energy demand according to various conditions. In this paper, we propose a method to predict energy demand in various situations using a deep learning model based on an autoencoder. This model consists of a projector that defines an appropriate state for a given situation and a predictor that forecasts energy demand from the defined state. The proposed model produces consumption predictions for 15, 30, 45, and 60 minutes with 60-minute demand to date. In the experiments with household electric power consumption data for five years, this model not only has a better performance with a mean squared error of 0.384 than the conventional models, but also improves the capacity to explain the results of prediction by visualizing the state with t-SNE algorithm. Despite unsupervised representation learning, we confirm that the proposed model defines the state well and predicts the energy demand accordingly.

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“…It has been proven to be effective for many fields, e.g., fault diagnosis [18], pattern recognition [19], and time series forecast [20,21]. Compared with the "shallow" models, DL has many hierarchical levels in a hidden layer, that is, the information representation is delivered from lower levels to higher levels, which makes the information representation more abstract and nonlinear for the higher levels.…”

Section: Methodologiesmentioning

confidence: 99%

Manufacturing Quality Prediction Using Intelligent Learning Approaches: A Comparative Study

et al. 2017

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Abstract:Under the international background of the transformation and promotion of manufacturing, the Chinese government proposed the "Made in China 2025" strategy, which focused on the improvement of a quality-based innovation ability. Moreover, predicting manufacturing quality is one of the crucial measures for quality management. Accurate prediction is closely related to the feature learning of manufacturing processes. Therefore, two categories of intelligent learning approaches, i.e., shallow learning and deep learning, are investigated and compared for manufacturing quality prediction in this paper. Specifically, the feed forward neural network (FFNN) with one hidden layer and the least squares support vector machine (LSSVM) with no hidden layers are selected as the representatives for shallow learning, and the deep restricted Boltzmann machine (DRBM) and the stack autoencoder (SAE) are chosen as the representatives for deep learning. The manufacturing data is collected from a competition about manufacturing quality control in the Tianchi Data Lab of China. The experiments show that the deep framework overwhelms the shallow architecture in terms of mean absolute percentage error, root-mean-square error, and threshold statistics. In addition, the prediction results also indicate that the performances depend on the length of the training data. That is, the bigger the sample size is, the better the performance is.

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Using Deep Learning Techniques to Forecast Environmental Consumption Level

Cited by 34 publications

References 37 publications

Deep Learning Approach for Forecasting Water Quality in IoT Systems

Deep Learning Approach for Forecasting Water Quality in IoT Systems

Electric Energy Consumption Prediction by Deep Learning with State Explainable Autoencoder

Manufacturing Quality Prediction Using Intelligent Learning Approaches: A Comparative Study

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