scholarly journals Simulation Study on the Effect of Reduced Inputs of Artificial Neural Networks on the Predictive Performance of the Solar Energy System

2017 ◽  
Vol 9 (8) ◽  
pp. 1382 ◽  
Author(s):  
Wahiba Yaïci ◽  
Michela Longo ◽  
Evgueniy Entchev ◽  
Federica Foiadelli
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Solar Energy ◽  
Simulation Study ◽  
Predictive Performance ◽  
Energy System ◽  
Artificial Neural

Predictive performance modeling of Habesha brewery wastewater treatment plant using artificial neural networks

2019 ◽  
Author(s):  
Chem Int
Keyword(s):  
Neural Networks ◽  
Water Quality ◽  
Wastewater Treatment ◽  
Artificial Neural Networks ◽  
Performance Modeling ◽  
Predictive Performance ◽  
Mathematical Tool ◽  
Effluent Water ◽  
Artificial Neural

Recently, process control in wastewater treatment plants (WWTPs) is, mostly accomplished through examining the quality of the water effluent and adjusting the processes through the operator’s experience. This practice is inefficient, costly and slow in control response. A better control of WTPs can be achieved by developing a robust mathematical tool for performance prediction. Due to their high accuracy and quite promising application in the field of engineering, Artificial Neural Networks (ANNs) are attracting attention in the domain of WWTP predictive performance modeling. This work focuses on applying ANN with a feed-forward, back propagation learning paradigm to predict the effluent water quality of the Habesha brewery WTP. Data of influent and effluent water quality covering approximately an 11-month period (May 2016 to March 2017) were used to develop, calibrate and validate the models. The study proves that ANN can predict the effluent water quality parameters with a correlation coefficient (R) between the observed and predicted output values reaching up to 0.969. Model architecture of 3-21-3 for pH and TN, and 1-76-1 for COD were selected as optimum topologies for predicting the Habesha Brewery WTP performance. The linear correlation between predicted and target outputs for the optimal model architectures described above were 0.9201 and 0.9692, respectively.

scholarly journals Simulation Study on the Application of the Generalized Entropy Concept in Artificial Neural Networks

Entropy ◽  
2018 ◽  
Vol 20 (4) ◽  
pp. 249 ◽  
Author(s):  
Krzysztof Gajowniczek ◽  
Arkadiusz Orłowski ◽  
Tomasz Ząbkowski
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Simulation Study ◽  
Generalized Entropy ◽  
Artificial Neural

A New Approach for Meteorological Variables Prediction at Kuala Lumpur, Malaysia, Using Artificial Neural Networks: Application for Sizing and Maintaining Photovoltaic Systems

2012 ◽  
Vol 134 (2) ◽  
Author(s):  
Tamer Khatib ◽  
Azah Mohamed ◽  
M. Mahmoud ◽  
K. Sopian
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Wind Speed ◽  
Relative Humidity ◽  
Solar Energy ◽  
Ambient Temperature ◽  
Meteorological Variables ◽  
Mean Bias Error ◽  
Artificial Neural ◽  
Prediction Approach

This research presents a new meteorological variables prediction approach for Malaysia using artificial neural networks. The developed model predicts four meteorological variables using sun shine ratio, day number, and location coordinates. These meteorological variables are solar energy, ambient temperature, wind speed, and relative humidity. However, three statistical values are used to evaluate the proposed model. These statistical values are mean absolute percentage error (MAPE), mean bias error (MBE), and root mean square error (RMSE). Based on results, the developed model predicts accurately the four meteorological variables. The MAPE, RMSE, and MBE in predicting solar radiation are 1.3%, 5.8 (1.8%), and 0.9 (0.3%), respectively, while the MAPE, RMSE, and MBE values for ambient temperature prediction are 1.3%, 0.4 (1.7%), and 0.1 (0.4%), respectively. In addition, the MAPE, RMSE, and MBE values in relative humidity prediction are 3.2%, 3.2, and 0.2. As for wind speed prediction, it is the worst in accuracy among the predicted variables because the MAPE, RMSE, and MBE values are 28.9%, 0.5 (31.3%), and 0.02 (1.25%). Such a developed model helps in sizing photovoltaic (PV) systems using solar energy and ambient temperature records. Moreover, wind speed and relative humidity records could be used in estimating dust concentration group which leads to dust deposition on a PV system.

scholarly journals Modeling Semiarid River–Aquifer Systems with Bayesian Networks and Artificial Neural Networks

Mathematics ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 107
Author(s):  
Ana D. Maldonado ◽  
María Morales ◽  
Francisco Navarro ◽  
Francisco Sánchez-Martos ◽  
Pedro A. Aguilera
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Bayesian Networks ◽  
Structure Learning ◽  
Computational Cost ◽  
Predictive Performance ◽  
Water Infiltration ◽  
Groundwater Temperature ◽  
Aquifer Systems ◽  
Artificial Neural

In semiarid areas, precipitations usually appear in the form of big and brief floods, which affect the aquifer through water infiltration, causing groundwater temperature changes. These changes may have an impact on the physical, chemical and biological processes of the aquifer and, thus, modeling the groundwater temperature variations associated with stormy precipitation episodes is essential, especially since this kind of precipitation is becoming increasingly frequent in semiarid regions. In this paper, we compare the predictive performance of two popular tools in statistics and machine learning, namely Bayesian networks (BNs) and artificial neural networks (ANNs), in modeling groundwater temperature variation associated with precipitation events. More specifically, we trained a total of 2145 ANNs with different node configurations, from one to five layers. On the other hand, we trained three different BNs using different structure learning algorithms. We conclude that, while both tools are equivalent in terms of accuracy for predicting groundwater temperature drops, the computational cost associated with the estimation of Bayesian networks is significantly lower, and the resulting BN models are more versatile and allow a more detailed analysis.

Diagnostic Cost Reduction Using Artificial Neural Networks

2011 ◽  
pp. 1812-1830
Author(s):  
Steven Walczak ◽  
Bradley B. Brimhall ◽  
Jerry B. Lefkowitz
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Clinical Laboratory ◽  
Predictive Performance ◽  
Diagnostic Model ◽  
Medical Problems ◽  
Ann Models ◽  
Artificial Neural ◽  
Diagnostic Capabilities ◽  
Performance Results

Patients face a multitude of diseases, trauma, and related medical problems that are difficult and costly to diagnose with respect to direct costs, including pulmonary embolism (PE). Advanced decision-making tools such as artificial neural networks (ANNs) improve diagnostic capabilities for these problematic medical conditions. The research in this chapter develops a backpropagation trained ANN diagnostic model to predict the occurrence of PE. Laboratory database values for 292 patients who were determined to be at risk for a PE, with 15% suffering a confirmed PE, are collected and used to evaluate various ANN models’ performance. Results indicate that using ANN diagnostic models enables the leveraging of knowledge gained from standard clinical laboratory tests, significantly improving both overall positive predictive and negative predictive performance.

Sign in / Sign up

Export Citation Format

Share Document