Weather Analysis Using Neural Networks for Modular Data Centers

2018 ◽  
Author(s):  
Feyisola Adejokun ◽  
Ashwin Siddarth ◽  
Abhishek Guhe ◽  
Dereje Agonafer
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Data Center ◽  
Evaporative Cooling ◽  
Weather Conditions ◽  
Cooling Control ◽  
The Neural Network ◽  
Modular Data

The objective of this work is to introduce the application of an artificial neural network (ANN) to assist in the evaporative cooling in data centers. To achieve this task, we employ the neural network algorithms to predict weather conditions outside the data center for direct evaporative cooling (DEC) operations. The predictive analysis helps optimize the cooling control strategy for maximizing the usage of evaporative cooling thereby improving the efficiency of the overall data center cooling system. A typical artificial neural network architecture is dynamic in nature and can perform adaptive learning in minimal computation time. A neural network model of a data center was created using operational historical data collected from a data center cooling control system. The neural network model allows the control of the modular data center (MDC) cooling at optimum configuration in two ways. First way is that the network model minimizes time delay for switching the cooling from one mode to the other. Second way, it improves the reaction behavior of the cooling equipment if an unexpected ambient condition change should come. The data center in consideration is a test bed modular data center that comprises of information Technology (IT) racks, Direct Evaporative cooling (DEC) and Indirect Evaporative Cooling (IEC) modules; the DEC/IEC are used together or in alternative mode to cool the data center room. The facility essentially utilizes outside ambient temperature and humidity conditions that are further conditioned by the DEC and IEC to cool the electronics, a concept know as air-side economization. Various parameters are related to the cooling system operation such as outside air temperature, IT heat load, cold aisle temperature, cold aisle humidity etc. are considered. Some of these parameters are fed into the artificial neural network as inputs and some are set as targets to train the neural network system. After the training the process is completed, certain bucket of data is tested and further used to validate the outputs for various other weather conditions. To make sure the analysis represents real world scenario, the operational data used are from real time data logged on the MDC cooling control unit. Overall, the neural network model is trained and is used to successfully predict the weather conditions and cooling control parameters. The prediction models have been demonstrated for the outputs that are static in nature (Levenberg Marquardt method) as well as the outputs that are dynamic in nature i.e., step-ahead & multistep ahead techniques.

scholarly journals PERANCANGAN MODEL UNTUK PERAMALAN TINGKAT PERTUMBUHAN PEMBIAYAAN MELALUI METODE JARINGAN SARAF TIRUAN DI BNI SYARIAH

2020 ◽  
Vol 6 (2) ◽  
Author(s):  
Orfyanny S Themba ◽  
Susianah Mokhtar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Data Bank ◽  
Forecasting Accuracy ◽  
The Neural Network ◽  
Forecasting Method ◽  
Artificial Neural

ABSTRAKTren perkembangan pembiayaan di Indonesia mulai meningkat namun cenderung melambat dari tahun ke tahun. Peramalan pertumbuhan pembiayaan pada bank syariah menjadi hal yang menarik karena naik turunnya pembiayaan akan berdampak pada perekonomian Indonesia. Tujuan dari penelitian ini melakukan peramalan pertumbuhan pembiayaan dalam jangka waktu setahun melalui metode Jaringan Saraf Tiruan pada data Bank BNI Syariah dari tahun 2015 sampai dengan 2019. Hasil dari peramalan diharapkan memberi informasi bagi bank untuk menunjang pengambilan keputusan dan menyiapkan strategi meningkatkan pembiayaan sehingga semakin besar laba yang akan diperoleh. Model peramalan dibuat berdasarkan metode peramalan dan ditujukan untuk digunakan pada aplikasi peramalan pembiayaan. Model Jaringan Saraf Tiruan memiliki nilai akurasi peramalan yang tinggi karena memiliki nilai error RMSE, MAPE yang minimum. Dari hasil peramalan menggunakan model Jaringan Saraf Tiruan menunjukkan terjadi peningkatan pembiayaan pada setiap bulannya untuk akad murabahah, mudharabah, musyarakah dan qardh. Hanya pembiayaan yang menggunakan ijarah yang mengalami penurunan drastis dibanding tahun-tahun sebelumnya. Pembiayaan murabahah masih tetap mendominasi dibanding akad mudharabah, musyarakah, qardh dan ijarah selama tahun 2020 Kata Kunci: Jaringan Saraf Tiruan ;PembiayaanABSTRACT Trend of financing development in Indonesia is starting to increase but tends to slow down from year to year. It is interesting to forecast the growth of financing in Islamic banks because the up and down of financing will have an impact on the Indonesian economy. The purpose of this study to forecast financing growth within a year through the Neural Network method on BNI Syariah Bank data from 2015 to 2019. The results of the forecast are expected to provide information for banks to support decision making and prepare strategies to increase financing so that greater profits that will be obtained. The forecasting model is made based on the forecasting method and is intended for use in financing forecasting applications. The Artificial Neural Network Model has a high value of forecasting accuracy because it has a minimum error value of RMSE, MAPE. The results of forecasting using the Artificial Neural Network model show an increase in financing every month for murabahah, mudharabah, musyarakah and qardh contracts. Only financing using ijarah has experienced a drastic decline compared to previous years. Murabahah financing still dominates over the mudharabah, musyarakah, qardh and ijarah contracts during 2020Keyword: Arificial Neural Network ;Financing

scholarly journals Modeling of a Novel Coaxial Ducted Fan Aerial Robot Combined with Corner Environment by Using Artificial Neural Network

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5805
Author(s):  
Tianfu Ai ◽  
Bin Xu ◽  
Changle Xiang ◽  
Wei Fan ◽  
Yibo Zhang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Simulation Software ◽  
Ducted Fan ◽  
The Neural Network ◽  
Aerial Robot ◽  
Artificial Neural ◽  
Comprehensive Modeling

A novel coaxial ducted fan aerial robot with a manipulator is proposed which can achieve some hover operation tasks in a corner environment, such as switching on and off a wall-attached button on the corner. In order to study the aerodynamic interference between the prototype and the environment when the aerial robot is hovering in the corner environment, a method for the comprehensive modeling of the prototype and corner environment based on the artificial neural network is presented. By using the CFD simulation software, the flow field of the prototype at different positions with the corner effect is analyzed. After determining the input, output and structure of the neural network model, the Adam and gradient descent algorithms are selected as the neural network training algorithms, respectively. In addition, to optimize the initial weights and biases of the neural network model, the genetic algorithm is precisely used. The three-dimensional prediction surfaces generated by the three methods of the neural network, kriging surface and the polynomial fitting are compared. The results show that the neural network has high prediction accuracy, and can be applied to the comprehensive modeling of the prototype and the corner environment.

scholarly journals Artificial Neural Network-Based Model for Prediction of Frost Heave Behavior of Silty Soil Specimen

2021 ◽  
Vol 11 (22) ◽  
pp. 10834
Author(s):  
Seok Yoon ◽  
Dinh-Viet Le ◽  
Gyu-Hyun Go
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Parametric Study ◽  
Mechanical Analysis ◽  
Frozen Soil ◽  
Frost Heave ◽  
The Neural Network ◽  
Artificial Neural

Frost heave action is a major issue in permafrost regions that can give rise to various geotechnical engineering problems. To analyze and predict this phenomenon at a specimen scale, this study conducted a fully coupled thermal-hydro-mechanical analysis and evaluated the frost heave behavior of frozen soil considering geotechnical parameters. Furthermore, a parametric study was performed to quantitatively analyze the effects of major geotechnical properties on frost heave behavior. According to the results of the parametric study, the amount of heave tended to decrease as the particle thermal conductivity increased, whereas the frost heave ratio tended to increase as the initial hydraulic conductivity increased. After evaluating the sensitivity of each parameter to frost heave behavior through statistical analyses, an artificial neural network model was developed to practically predict frost heave behavior. According to the verification results of the neural network model, the trained network model demonstrated a reliable accuracy (R2 = 0.893) in predicting frost heave ratio, even when the model used test datasets that were not part of the training datasets.

scholarly journals Snack Texture Estimation System Using a Simple Equipment and Neural Network Model

2019 ◽  
Vol 11 (3) ◽  
pp. 68 ◽  
Author(s):  
Shigeru Kato ◽  
Naoki Wada ◽  
Ryuji Ito ◽  
Takaya Shiozaki ◽  
Yudai Nishiyama ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Convolutional Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Food Texture ◽  
The Neural Network ◽  
Equipment Unit ◽  
Estimation System

Texture evaluation is manually performed in general, and such analytical tasks can get cumbersome. In this regard, a neural network model is employed in this study. This paper describes a system that can estimate the food texture of snacks. The system comprises a simple equipment unit and an artificial neural network model. The equipment simultaneously examines the load and sound when a snack is pressed. The neural network model analyzes the load change and sound signals and then outputs a numerical value within the range (0,1) to express the level of textures such as “crunchiness” and “crispness”. Experimental results validate the model’s capacity to output moderate texture values of the snacks. In addition, we applied the convolutional neural network (CNN) model to classify snacks and the capability of the CNN model for texture estimation is discussed.

scholarly journals Thermal Conductivity Modeling of Propylene Glycol - Based Nanofluid Using Artificial Neural Network

2018 ◽  
Vol 14 (1) ◽  
pp. 5281-5291 ◽  
Author(s):  
R. A. Mohamed ◽  
D. M. Habashy
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Thermal Conductivity ◽  
Artificial Neural Network ◽  
Propylene Glycol ◽  
Network Model ◽  
Neural Network Model ◽  
Network System ◽  
The Neural Network ◽  
Neural Network System

The article introduces artificial neural network model that simulates and predicts thermal conductivity and particle size of propylene glycol - based nanofluids containing Al2O3 and TiO2 nanoparticles in a temperature rang 20 - 80oc. The experimental data indicated that the nanofluids have excellent stability over the temperature scale of interest and thermal conductivity enhancement for both nanofluid samples. The neural network system was trained on the available experimental data. The system was designed to find the optimal network that has the best training performance. The nonlinear equations which represent the relation between the inputs and output were obtained. The results of neural network model and the theoretical models of the proposed system were performed and compared with the experimental results. The neural network system appears to yield the best fit consistent with experimental data. The results of the paper demonstrate the ability of neural network model as an excellent computational tool in nanofluid field.

scholarly journals Determination of daily solar ultraviolet radiation using statistical models and artificial neural networks

2006 ◽  
Vol 24 (8) ◽  
pp. 2105-2114 ◽  
Author(s):  
F. J. Barbero ◽  
G. López ◽  
F. J. Batlles
Keyword(s):  
Neural Network ◽  
Statistical Model ◽  
Network Model ◽  
Neural Network Model ◽  
Weather Conditions ◽  
Neural Network Models ◽  
Solar Ultraviolet Radiation ◽  
Air Mass ◽  
The Neural Network ◽  
Artificial Neural

Abstract. In this study, two different methodologies are used to develop two models for estimating daily solar UV radiation. The first is based on traditional statistical techniques whereas the second is based on artificial neural network methods. Both models use daily solar global broadband radiation as the only measured input. The statistical model is derived from a relationship between the daily UV and the global clearness indices but modulated by the relative optical air mass. The inputs to the neural network model were determined from a large number of radiometric and atmospheric parameters using the automatic relevance determination method, although only the daily solar global irradiation, daily global clearness index and relative optical air mass were shown to be the optimal input variables. Both statistical and neural network models were developed using data measured at Almería (Spain), a semiarid and coastal climate, and tested against data from Table Mountain (Golden, CO, USA), a mountainous and dry environment. Results show that the statistical model performs adequately in both sites for all weather conditions, especially when only snow-free days at Golden were considered (RMSE=4.6%, MBE= –0.1%). The neural network based model provides the best overall estimates in the site where it has been trained, but presents an inadequate performance for the Golden site when snow-covered days are included (RMSE=6.5%, MBE= –3.0%). This result confirms that the neural network model does not adequately respond on those ranges of the input parameters which were not used for its development.

scholarly journals An Artificial Neural Network For Hedging Crude Oil

2011 ◽  
Vol 3 (1) ◽  
Author(s):  
Hossein Najafi ◽  
Reza Rahgozar ◽  
Brian Champlin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Crude Oil ◽  
Network Model ◽  
Neural Network Model ◽  
Price Risk ◽  
Futures Prices ◽  
Font Size ◽  
Empirical Results ◽  
The Neural Network

<p class="MsoBodyText" style="text-align: justify; margin: 0in 34.9pt 0pt 35.5pt; mso-pagination: none;"><span style="font-size: 10pt;"><span style="font-family: Times New Roman;">A neural network model for hedging crude oil is introduced.<span style="mso-spacerun: yes;">&nbsp; </span>The NYMEX futures prices is used to investigate the effectiveness of this model. Empirical results show that the neural network model reduces price risk more than other approaches. </span></span></p>

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