scholarly journals A Novel Analytical-ANN Hybrid Model for Borehole Heat Exchanger

Energies ◽  
2020 ◽  
Vol 13 (23) ◽  
pp. 6213
Author(s):  
Anjan Rao Puttige ◽  
Staffan Andersson ◽  
Ronny Östin ◽  
Thomas Olofsson
Keyword(s):  
Neural Network ◽  
Heat Exchanger ◽  
Analytical Model ◽  
Hybrid Model ◽  
Time Resolution ◽  
Network Models ◽  
Borehole Heat Exchanger ◽  
Neural Network Models ◽  
Ground Source

Optimizing the operation of ground source heat pumps requires simulation of both short-term and long-term response of the borehole heat exchanger. However, the current physical and neural network based models are not suited to handle the large range of time scales, especially for large borehole fields. In this study, we present a hybrid model for long-term simulation of BHE with high resolution in time. The model uses an analytical model with low time resolution to guide an artificial neural network model with high time resolution. We trained, tuned, and tested the hybrid model using measured data from a ground source heat pump in real operation. The performance of the hybrid model is compared with an analytical model, a calibrated analytical model, and three different types of neural network models. The hybrid model has a relative RMSE of 6% for the testing period compared to 22%, 14%, and 12% respectively for the analytical model, the calibrated analytical model, and the best of the three investigated neural network models. The hybrid model also has a reasonable computational time and was also found to be robust with regard to the model parameters used by the analytical model.

scholarly journals Modelling Particulate Matter Using Multivariate and Multistep Recurrent Neural Networks

2021 ◽  
Vol 9 ◽  
Author(s):  
Tushar Saini ◽  
Pratik Chaturvedi ◽  
Varun Dutt
Keyword(s):  
Neural Network ◽  
Air Quality ◽  
Recurrent Neural Network ◽  
Network Models ◽  
Economic Losses ◽  
Ensemble Model ◽  
Neural Network Models ◽  
Ensemble Techniques ◽  
Bidirectional Lstm

Air quality is a major problem in the world, having severe health implications. Long-term exposure to poor air quality causes pulmonary and cardiovascular diseases. Several studies have also found that deteriorating air quality also causes substantial economic losses. Thus, techniques that can forecast air quality with higher accuracy may help reduce health and economic consequences. Prior research has utilized state-of-the-art artificial neural network and recurrent neural network models for forecasting air quality. However, a comprehensive investigation of different architectures of recurrent neural network, especially LSTMs and ensemble techniques, has been less explored. Also, there have been less explorations of long-term air quality forecasts via these methods exists. This research proposes the development and calibration of recurrent neural network models and their ensemble, which can forecast air quality in terms of PM2.5 concentration 6 hours ahead in time. For forecasting air quality, a vanilla-LSTM, a stack-LSTM, a bidirectional-LSTM, a CNN-LSTM, and an ensemble of individual LSTM models were trained on the UCI Machine Learning Beijing dataset. Data were split into two parts, where 80% of data were used for training the models, while the remaining 20% were used for validating the models. For comparative analysis, four regression losses were calculated, namely root mean squared error, mean absolute percentage error, mean absolute error and Pearson’s correlation coefficient. Results revealed that among all models, the ensemble model performed the best in predicting the PM2.5 concentrations. Furthermore, the ensemble model outperformed other models reported in literature by a long margin. Among the individual models, the bidirectional-LSTM performed the best. We highlight the implications of this research on long-term forecasting of air quality via recurrent and ensemble techniques.

A Study on the Contribution of 12 Key-Factors to the Growth Rates of the Region of the East Macedonia-Thrace (EMTH) by Using a Neural Network Model

2012 ◽  
Vol 1 (1) ◽  
pp. 18-28
Author(s):  
E. Stathakis ◽  
M. Hanias ◽  
P. Antoniades ◽  
L. Magafas ◽  
D. Bandekas
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Regional Growth ◽  
Network Models ◽  
Domestic Product ◽  
Key Factors ◽  
Neural Network Models ◽  
First Time

This study gives a new methodological framework regarding the measuring of the contribution of some key-factors on the regional growth rate and forecasting the future development rates, based on Neural Network Models (NN Models). It’s a serious attempt to study the contribution of twelve key-factors to the change of the Regional Gross Domestic Product of the Region of East Macedonia -Thrace during a long-term of growth process, by creating and using a suitable Neural Network Model. Specifically, twelve key-factors, time functioned in the period 1991-2008, are studied for the first time, in order to be investigated, scientifically, firstly their % contribution to growth of the regional economy and secondly, to be predicted how much the (Regional Growth Domestic Product) RGDP-under certain conditions-will be changed. It’s a NN Model with inputs the twelve key-factors in order to be evaluated and measured, at the best precise, their percentage contribution to the RGDP. The model and results can be found further into the article.

scholarly journals Advanced Convolutional Neural Network-Based Hybrid Acoustic Models for Low-Resource Speech Recognition

Computers ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 36
Author(s):  
Tessfu Geteye Fantaye ◽  
Junqing Yu ◽  
Tulu Tilahun Hailu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Speech Recognition ◽  
Convolutional Neural Network ◽  
Network Models ◽  
Neural Network Models ◽  
Feed Forward ◽  
Acoustic Models ◽  
Low Resource

Deep neural networks (DNNs) have shown a great achievement in acoustic modeling for speech recognition task. Of these networks, convolutional neural network (CNN) is an effective network for representing the local properties of the speech formants. However, CNN is not suitable for modeling the long-term context dependencies between speech signal frames. Recently, the recurrent neural networks (RNNs) have shown great abilities for modeling long-term context dependencies. However, the performance of RNNs is not good for low-resource speech recognition tasks, and is even worse than the conventional feed-forward neural networks. Moreover, these networks often overfit severely on the training corpus in the low-resource speech recognition tasks. This paper presents the results of our contributions to combine CNN and conventional RNN with gate, highway, and residual networks to reduce the above problems. The optimal neural network structures and training strategies for the proposed neural network models are explored. Experiments were conducted on the Amharic and Chaha datasets, as well as on the limited language packages (10-h) of the benchmark datasets released under the Intelligence Advanced Research Projects Activity (IARPA) Babel Program. The proposed neural network models achieve 0.1–42.79% relative performance improvements over their corresponding feed-forward DNN, CNN, bidirectional RNN (BRNN), or bidirectional gated recurrent unit (BGRU) baselines across six language collections. These approaches are promising candidates for developing better performance acoustic models for low-resource speech recognition tasks.

scholarly journals Time-evolving Text Classification with Deep Neural Networks

2018 ◽  
Author(s):  
Yu He ◽  
Jianxin Li ◽  
Yangqiu Song ◽  
Mutian He ◽  
Hao Peng
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Text Classification ◽  
Network Models ◽  
Short Term ◽  
Neural Network Models ◽  
Evolutionary Neural Network ◽  
Learned Features ◽  
Term Fluctuation

Traditional text classification algorithms are based on the assumption that data are independent and identically distributed. However, in most non-stationary scenarios, data may change smoothly due to long-term evolution and short-term fluctuation, which raises new challenges to traditional methods. In this paper, we present the first attempt to explore evolutionary neural network models for time-evolving text classification. We first introduce a simple way to extend arbitrary neural networks to evolutionary learning by using a temporal smoothness framework, and then propose a diachronic propagation framework to incorporate the historical impact into currently learned features through diachronic connections. Experiments on real-world news data demonstrate that our approaches greatly and consistently outperform traditional neural network models in both accuracy and stability.

Long-Term Wind Speed and Power Forecasting Using Local Recurrent Neural Network Models

2006 ◽  
Vol 21 (1) ◽  
pp. 273-284 ◽  
Author(s):  
T.G. Barbounis ◽  
J.B. Theocharis ◽  
M.C. Alexiadis ◽  
P.S. Dokopoulos
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Recurrent Neural Network ◽  
Network Models ◽  
Neural Network Models ◽  
Power Forecasting

scholarly journals Forecasting stock index based on hybrid artificial neural network models

2019 ◽  
Vol 3 (1) ◽  
pp. 52-57
Author(s):  
Ta Quoc Bao ◽  
Le Nhat Tan ◽  
Le Thi Thanh An ◽  
Bui Thi Thien My
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Hybrid Model ◽  
Arima Model ◽  
Network Models ◽  
Stock Index ◽  
Ann Model ◽  
Neural Network Models ◽  
Artificial Neural

Forecasting stock index is a crucial financial problem which is recently received a lot of interests in the field of artificial intelligence. In this paper we are going to study some hybrid artificial neural network models. As main result, we show that hybrid models offer us effective tools to forecast stock index accurately. Within this study, we have analyzed the performance of classical models such as Autoregressive Integrated Moving Average (ARIMA), Artificial Neural Network (ANN) model and the Hybrid model, in connection with real data coming from Vietnam Index (VNINDEX). Based on some previous foreign data sets, for most of the complex time series, the novel hybrid models have a good performance comparing to individual models like ARIMA and ANN. Regarding Vietnamese stock market, our results also show that the Hybrid model gives much better forecasting accuracy compared with ARIMA and ANN models. Specifically, our results tell that the Hybrid combination model delivers smaller Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) than ARIMA and ANN models. The fitting curves demonstrate that the Hybrid model produces closer trend so better describing the actual data. Via our study with Vietnam Index, it is confirmed that the characteristics of ARIMA model are more suitable for linear time series while ANN model is good to work with nonlinear time series. The Hybrid model takes into account both of these features, so it could be employed in case of more generalized time series. As the financial market is increasingly complex, the time series corresponding to stock indexes naturally consist of linear and non-linear components. Because of these characteristic, the Hybrid ARIMA model with ANN produces better prediction and estimation than other traditional models.  

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