scholarly journals Modeling Carbon Release of Brazilian Highest Economic Pole and Major Urban Emitter: Comparing Classical Methods and Artificial Neural Networks

Climate ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 9
Author(s):  
Daniela Debone ◽  
Tiago Dias Martins ◽  
Simone Georges El Khouri Miraglia

Despite the concern about climate change and the associated negative impacts, fossil fuels continue to prevail in the global energy consumption. This paper aimed to propose the first model that relates CO2 emissions of Sao Paulo, the main urban center emitter in Brazil, with gross national product and energy consumption. Thus, we investigated the accuracy of three different methods: multivariate linear regression, elastic-net regression, and multilayer perceptron artificial neural networks. Comparing the results, we clearly demonstrated the superiority of artificial neural networks when compared with the other models. They presented better results of mean absolute percentage error (MAPE = 0.76%) and the highest possible coefficient of determination (R2 = 1.00). This investigation provides an innovative integrated climate-economic approach for the accurate prediction of carbon emissions. Therefore, it can be considered as a potential valuable decision-support tool for policymakers to design and implement effective environmental policies.

Author(s):  
P W Ilott ◽  
A J Griffiths

Pumping costs within British industry are enormous, with the potential for considerable financial savings through fault diagnosis and condition-based maintenance. Accurate condition monitoring data interpretation is a key requirement in pump fault diagnosis. However, the human skills required to transform monitored data into maintenance information are often unavailable. Artificial neural networks (ANNs) are proposed for automation of this skill in the development of a pumping system decision support tool, the key requirement of which is accurate pump fault diagnosis. The cumulative sum charting procedure was used to establish a knowledge base of fault data for ANN implementation based on historical parameter measurements. Various preprocessing techniques were investigated in relation to generalization ability and convergence rates during the learning phase. Preprocessing predominantly aVected ANN convergence rate, with the quality of training data crucial to generalization ability. ANNs could provide accurate, incipient fault diagnosis of pumping machinery based on real industrial data corresponding to historical pump faults.


Author(s):  
Bhargavi Munnaluri ◽  
K. Ganesh Reddy

Wind forecasting is one of the best efficient ways to deal with the challenges of wind power generation. Due to the depletion of fossil fuels renewable energy sources plays a major role for the generation of power. For future management and for future utilization of power, we need to predict the wind speed.  In this paper, an efficient hybrid forecasting approach with the combination of Support Vector Machine (SVM) and Artificial Neural Networks(ANN) are proposed to improve the quality of prediction of wind speed. Due to the different parameters of wind, it is difficult to find the accurate prediction value of the wind speed. The proposed hybrid model of forecasting is examined by taking the hourly wind speed of past years data by reducing the prediction error with the help of Mean Square Error by 0.019. The result obtained from the Artificial Neural Networks improves the forecasting quality.


Agriculture ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 567
Author(s):  
Jolanta Wawrzyniak

Artificial neural networks (ANNs) constitute a promising modeling approach that may be used in control systems for postharvest preservation and storage processes. The study investigated the ability of multilayer perceptron and radial-basis function ANNs to predict fungal population levels in bulk stored rapeseeds with various temperatures (T = 12–30 °C) and water activity in seeds (aw = 0.75–0.90). The neural network model input included aw, temperature, and time, whilst the fungal population level was the model output. During the model construction, networks with a different number of hidden layer neurons and different configurations of activation functions in neurons of the hidden and output layers were examined. The best architecture was the multilayer perceptron ANN, in which the hyperbolic tangent function acted as an activation function in the hidden layer neurons, while the linear function was the activation function in the output layer neuron. The developed structure exhibits high prediction accuracy and high generalization capability. The model provided in the research may be readily incorporated into control systems for postharvest rapeseed preservation and storage as a support tool, which based on easily measurable on-line parameters can estimate the risk of fungal development and thus mycotoxin accumulation.


Author(s):  
Sankhanil Goswami

Abstract Modern buildings account for a significant proportion of global energy consumption worldwide. Therefore, accurate energy use forecast is necessary for energy management and conservation. With the advent of smart sensors, a large amount of accurate energy data is available. Also, with the advancements in data analytics and machine learning, there have been numerous studies on developing data-driven prediction models based on Artificial Neural Networks (ANNs). In this work a type of ANN called Large Short-Term Memory (LSTM) is used to predict the energy use and cooling load of an existing building. A university administrative building was chosen for its typical commercial environment. The network was trained with one year of data and was used to predict the energy consumption and cooling load of the following year. The mean absolute testing error for the energy consumption and the cooling load were 0.105 and 0.05. The percentage mean accuracy was found to be 92.8% and 96.1%. The process was applied to several other buildings in the university and similar results were obtained. This indicates the model can successfully predict the energy consumption and cooling load for the buildings studied. The further improvement and application of this technique for optimizing building performance are also explored.


2013 ◽  
Vol 11 (12) ◽  
pp. 2333-2340 ◽  
Author(s):  
Feng-Kuang Chuang ◽  
Chih-Young Hung ◽  
Chi-Ya Chang ◽  
Kuo-Cheng Kuo

2011 ◽  
Vol 314-316 ◽  
pp. 547-553
Author(s):  
Peng Fei Zhu ◽  
Xiao Fang Sun ◽  
Ying Jun Lu ◽  
Hai Tian Pan

A feed-forward three-layer neural network was proposed to predict the fracture force of injection-molded parts’ weld line. Firstly, the most significant process parameters which affect the fracture force of weld line were analyzed. Secondly, melt temperature, injection pressure, holding pressure and holding time were chosen as import variables and the fracture force of weld line was chosen as output variable to construct artificial neural networks. Furthermore, the performance of ANN was evaluated and tested by its application to verification tests with process parameters randomly selected which all of them were not used in the network training. Results showed that the ANN predictions yield mean absolute percentage error (MAPE) in the range of 0.86%,and maximum relative error (MRE) in the range of 1.84% for the test data set, and which can comparatively accurately reflect the influence relation of the injection process parameters on part’s quality index under the circumstance of data deficiencies.


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