scholarly journals Forecasting Energy Consumption of Wastewater Treatment Plants with a Transfer Learning Approach for Sustainable Cities

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1149
Author(s):  
Pedro Oliveira ◽  
Bruno Fernandes ◽  
Cesar Analide ◽  
Paulo Novais
Keyword(s):  
Wastewater Treatment ◽  
Energy Consumption ◽  
Transfer Learning ◽  
Short Term Memory ◽  
Wastewater Treatment Plants ◽  
Learning Approach ◽  
Sustainable Cities ◽  
Short Term ◽  
Long Short Term Memory ◽  
Gated Recurrent Units

A major challenge of today’s society is to make large urban centres more sustainable. Improving the energy efficiency of the various infrastructures that make up cities is one aspect being considered when improving their sustainability, with Wastewater Treatment Plants (WWTPs) being one of them. Consequently, this study aims to conceive, tune, and evaluate a set of candidate deep learning models with the goal being to forecast the energy consumption of a WWTP, following a recursive multi-step approach. Three distinct types of models were experimented, in particular, Long Short-Term Memory networks (LSTMs), Gated Recurrent Units (GRUs), and uni-dimensional Convolutional Neural Networks (CNNs). Uni- and multi-variate settings were evaluated, as well as different methods for handling outliers. Promising forecasting results were obtained by CNN-based models, being this difference statistically significant when compared to LSTMs and GRUs, with the best model presenting an approximate overall error of 630 kWh when on a multi-variate setting. Finally, to overcome the problem of data scarcity in WWTPs, transfer learning processes were implemented, with promising results being achieved when using a pre-trained uni-variate CNN model, with the overall error reducing to 325 kWh.

scholarly journals Crop Yield Estimation Using Deep Learning Based on Climate Big Data and Irrigation Scheduling

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3004
Author(s):  
Khadijeh Alibabaei ◽  
Pedro D. Gaspar ◽  
Tânia M. Lima
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Energy Consumption ◽  
Short Term Memory ◽  
Irrigation Scheduling ◽  
Climate Data ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Gated Recurrent Units

Deep learning has already been successfully used in the development of decision support systems in various domains. Therefore, there is an incentive to apply it in other important domains such as agriculture. Fertilizers, electricity, chemicals, human labor, and water are the components of total energy consumption in agriculture. Yield estimates are critical for food security, crop management, irrigation scheduling, and estimating labor requirements for harvesting and storage. Therefore, estimating product yield can reduce energy consumption. Two deep learning models, Long Short-Term Memory and Gated Recurrent Units, have been developed for the analysis of time-series data such as agricultural datasets. In this paper, the capabilities of these models and their extensions, called Bidirectional Long Short-Term Memory and Bidirectional Gated Recurrent Units, to predict end-of-season yields are investigated. The models use historical data, including climate data, irrigation scheduling, and soil water content, to estimate end-of-season yield. The application of this technique was tested for tomato and potato yields at a site in Portugal. The Bidirectional Long Short-Term memory outperformed the Gated Recurrent Units network, the Long Short-Term Memory, and the Bidirectional Gated Recurrent Units network on the validation dataset. The model was able to capture the nonlinear relationship between irrigation amount, climate data, and soil water content and predict yield with an MSE of 0.017 to 0.039. The performance of the Bidirectional Long Short-Term Memory in the test was compared with the most commonly used deep learning method, the Convolutional Neural Network, and machine learning methods including a Multi-Layer Perceptrons model and Random Forest Regression. The Bidirectional Long Short-Term Memory outperformed the other models with an R2 score between 0.97 and 0.99. The results show that analyzing agricultural data with the Long Short-Term Memory model improves the performance of the model in terms of accuracy. The Convolutional Neural Network model achieved the second-best performance. Therefore, the deep learning model has a remarkable ability to predict the yield at the end of the season.

scholarly journals Multiple Electric Energy Consumption Forecasting Using a Cluster-Based Strategy for Transfer Learning in Smart Building

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2668 ◽  
Author(s):  
Tuong Le ◽  
Minh Thanh Vo ◽  
Tung Kieu ◽  
Eenjun Hwang ◽  
Seungmin Rho ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Transfer Learning ◽  
Energy Management ◽  
Short Term Memory ◽  
Electric Energy ◽  
Computational Time ◽  
Short Term ◽  
Smart Building ◽  
Electric Energy Consumption ◽  
Long Short Term Memory

Electric energy consumption forecasting is an interesting, challenging, and important issue in energy management and equipment efficiency improvement. Existing approaches are predictive models that have the ability to predict for a specific profile, i.e., a time series of a whole building or an individual household in a smart building. In practice, there are many profiles in each smart building, which leads to time-consuming and expensive system resources. Therefore, this study develops a robust framework for the Multiple Electric Energy Consumption forecasting (MEC) of a smart building using Transfer Learning and Long Short-Term Memory (TLL), the so-called MEC-TLL framework. In this framework, we first employ a k-means clustering algorithm to cluster the daily load demand of many profiles in the training set. In this phase, we also perform Silhouette analysis to specify the optimal number of clusters for the experimental datasets. Next, this study develops the MEC training algorithm, which utilizes a cluster-based strategy for transfer learning the Long Short-Term Memory models to reduce the computational time. Finally, extensive experiments are conducted to compare the computational time and different performance metrics for multiple electric energy consumption forecasting on two smart buildings in South Korea. The experimental results indicate that our proposed approach is capable of economical overheads while achieving superior performances. Therefore, the proposed approach can be applied effectively for intelligent energy management in smart buildings.

A novel fuzzy rough set based long short-term memory integration model for energy consumption prediction of public buildings

2020 ◽  
pp. 1-15
Author(s):  
Hongchang Sun ◽  
Yadong wang ◽  
Lanqiang Niu ◽  
Fengyu Zhou ◽  
Heng Li
Keyword(s):  
Energy Consumption ◽  
Rough Set ◽  
Prediction Accuracy ◽  
Short Term Memory ◽  
Short Term ◽  
Fuzzy Rough Set ◽  
Proposed Model ◽  
Long Short Term Memory ◽  
Energy Consumption Prediction ◽  
Consumption Prediction

Building energy consumption (BEC) prediction is very important for energy management and conservation. This paper presents a short-term energy consumption prediction method that integrates the Fuzzy Rough Set (FRS) theory and the Long Short-Term Memory (LSTM) model, and is thus named FRS-LSTM. This method can find the most directly related factors from the complex and diverse factors influencing the energy consumption, which improves the prediction accuracy and efficiency. First, the FRS is used to reduce the redundancy of the input features by the attribute reduction of the factors affecting the energy consumption forecasting, and solves the data loss problem caused by the data discretization of a classical rough set. Then, the final attribute set after reduction is taken as the input of the LSTM networks to obtain the final prediction results. To validate the effectiveness of the proposed model, this study used the actual data of a public building to predict the building’s energy consumption, and compared the proposed model with the LSTM, Levenberg-Marquardt Back Propagation (LM-BP), and Support Vector Regression (SVR) models. The experimental results reveal that the presented FRS-LSTM model achieves higher prediction accuracy compared with other comparative models.

scholarly journals Electrocorticography based motor imagery movements classification using long short-term memory (LSTM) based on deep learning approach

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Mamunur Rashid ◽  
Minarul Islam ◽  
Norizam Sulaiman ◽  
Bifta Sama Bari ◽  
Ripon Kumar Saha ◽  
...  
Keyword(s):  
Deep Learning ◽  
Motor Imagery ◽  
Short Term Memory ◽  
Learning Approach ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

scholarly journals Soft Biometrics Classication in Videos Using Transfer Learning and Bidirectional Long Short-Term Memory Networks

2020 ◽  
Vol 18 (1) ◽  
pp. 47-59
Author(s):  
Marcelo Romero ◽  
Matheusq Gutoski ◽  
Leandro Takeshi Hattori ◽  
Manassés Ribeiro ◽  
Heitor S. Lopes
Keyword(s):  
Transfer Learning ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Soft Biometrics ◽  
Long Short Term Memory

scholarly journals A Hybrid Model for Power Consumption Forecasting Using VMD-Based the Long Short-Term Memory Neural Network

2022 ◽  
Vol 9 ◽  
Author(s):  
Yingjun Ruan ◽  
Gang Wang ◽  
Hua Meng ◽  
Fanyue Qian
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Power Consumption ◽  
Hybrid Model ◽  
Short Term Memory ◽  
Series Data ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Individual Features

Energy consumption prediction is a popular research field in computational intelligence. However, it is difficult for general machine learning models to handle complex time series data such as building energy consumption data, and the results are often unsatisfactory. To address this difficulty, a hybrid prediction model based on modal decomposition was proposed in this paper. For data preprocessing, the variational mode decomposition (VMD) technique was used to used to decompose the original sequence into more robust subsequences. In the feature selection, the maximum relevance minimum redundancy (mRMR) algorithm was chosen to analyse the correlation between each component and the individual features while eliminating the redundancy between individual features. In the forecasting module, the long short-term memory (LSTM) neural network model was used to predict power consumption. In order to verify the performance of the proposed model, three categories of contrast methods were applied: 1) Comparing the hybrid model to a single predictive model, 2) Comparing the hybrid model with the backpropagation neural network (BPNN) to the hybrid model with the LSTM and 3) Comparing the hybrid model using mRMR and the hybrid model using mutual information maximization (MIM). The experimental results on the measured data of an office building in Qingdao show that the proposed hybrid model can improve the prediction accuracy and has better robustness compared to VMD-MIM-LSTM. In the three control groups mentioned above, the R2 value of the hybrid model improved by 10, 3 and 3%, respectively, the values of the mean absolute error (MAE) decreased by 48.9, 41.4 and 35.6%, respectively, and the root mean square error (RMSE) decreased by 54.7, 35.5 and 34.1%, respectively.

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