scholarly journals A Novel Hybrid Fuel Consumption Prediction Model for Ocean-Going Container Ships Based on Sensor Data

2021 ◽  
Vol 9 (4) ◽  
pp. 449
Author(s):  
Zhihui Hu ◽  
Tianrui Zhou ◽  
Mohd Tarmizi Osman ◽  
Xiaohe Li ◽  
Yongxin Jin ◽  
...  
Keyword(s):  
Prediction Model ◽  
Real Time ◽  
Fuel Consumption ◽  
Hybrid Model ◽  
Predictive Performance ◽  
Optimization Method ◽  
Bayesian Optimization ◽  
Sensor Data ◽  
Single Model ◽  
Consumption Prediction

Accurate, reliable, and real-time prediction of ship fuel consumption is the basis and premise of the development of fuel optimization; however, ship fuel consumption data mainly come from noon reports, and many current modeling methods have been based on a single model; therefore they have low accuracy and robustness. In this study, we propose a novel hybrid fuel consumption prediction model based on sensor data collected from an ocean-going container ship. First, a data processing method is proposed to clean the collected data. Secondly, the Bayesian optimization method of hyperparameters is used to reasonably set the hyperparameter values of the model. Finally, a hybrid fuel consumption prediction model is established by integrating extremely randomized tree (ET), random forest (RF), Xgboost (XGB) and multiple linear regression (MLR) methods. The experimental results show that data cleaning, the size of the dataset, marine environmental factors, and hyperparameter optimization can all affect the accuracy of the model, and the proposed hybrid model provides better predictive performance (higher accuracy) and greater robustness (smaller standard deviation) as compared with a single model. The proposed hybrid model should play a significant role in ship fuel consumption real-time monitoring, fault diagnosis, energy saving and emission reduction, etc.

scholarly journals Modeling and Optimization for Piercing Efficiency and Energy Consumption Based on Mean Value Substaged KELM-PLS and GA Method

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Dong Xiao ◽  
Jichun Wang
Keyword(s):  
Energy Consumption ◽  
Prediction Model ◽  
Prediction Models ◽  
Optimization Method ◽  
Mean Value ◽  
Genetic Optimization ◽  
Simulation And Experiment ◽  
Coupling Characteristics ◽  
Energy Consumption Prediction ◽  
Consumption Prediction

Piercing manufacture of seamless tubes is the process that pierces solid blank into tube hollow. Piercing efficiency and energy consumption are the important indexes in the production of seamless tubes. Piercing process has the multivariate, nonlinear, cross-coupling characteristics. The complex factors that affect efficiency and consumption make it difficult to establish the mechanism models for optimization. Based on the production process, this paper divides the piercing process into three parts and proposes the piercing efficiency and energy consumption prediction models based on mean value staged KELM-PLS method. On the basis of mean value staged KELM-PLS prediction model, the minimum piercing energy consumption and maximum piercing efficiency are calculated by genetic optimization algorithm. Simulation and experiment prove that the optimization method based on the piercing efficiency and energy consumption prediction model can obtain the optimal process parameters effectively and also provide reliable evidences for practical production.

scholarly journals A GreyNGM(1,1,k)Self-Memory Coupling Prediction Model for Energy Consumption Prediction

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Xiaojun Guo ◽  
Sifeng Liu ◽  
Lifeng Wu ◽  
Lingling Tang
Keyword(s):  
Energy Consumption ◽  
Prediction Model ◽  
Electricity Consumption ◽  
Predictive Performance ◽  
Energy System ◽  
The Self ◽  
Grey Prediction ◽  
Stochastic Fluctuation ◽  
Energy Consumption Prediction ◽  
Consumption Prediction

Energy consumption prediction is an important issue for governments, energy sector investors, and other related corporations. Although there are several prediction techniques, selection of the most appropriate technique is of vital importance. As for the approximate nonhomogeneous exponential data sequence often emerging in the energy system, a novel greyNGM(1,1,k)self-memory coupling prediction model is put forward in order to promote the predictive performance. It achieves organic integration of the self-memory principle of dynamic system and greyNGM(1,1,k)model. The traditional grey model’s weakness as being sensitive to initial value can be overcome by the self-memory principle. In this study, total energy, coal, and electricity consumption of China is adopted for demonstration by using the proposed coupling prediction technique. The results show the superiority ofNGM(1,1,k)self-memory coupling prediction model when compared with the results from the literature. Its excellent prediction performance lies in that the proposed coupling model can take full advantage of the systematic multitime historical data and catch the stochastic fluctuation tendency. This work also makes a significant contribution to the enrichment of grey prediction theory and the extension of its application span.

scholarly journals Predicting Real-Time Crash Risk for Urban Expressways in China

2017 ◽  
Vol 2017 ◽  
pp. 1-10
Author(s):  
Miaomiao Liu ◽  
Yongsheng Chen
Keyword(s):  
Decision Tree ◽  
Prediction Model ◽  
Real Time ◽  
Risk Prediction ◽  
Fuzzy Inference ◽  
Risk Prediction Model ◽  
Training Data ◽  
Sensor Data ◽  
Crash Risk ◽  
Inference System

We developed a real-time crash risk prediction model for urban expressways in China in this study. About two-year crash data and their matching traffic sensor data from the Beijing section of Jingha expressway were utilized for this research. The traffic data in six 5-minute intervals between 0 and 30 minutes prior to crash occurrence was extracted, respectively. To obtain the appropriate data training period, the data (in each 5-minute interval) during six different periods was collected as training data, respectively, and the crash risk value under different data conditions was defined. Then we proposed a new real-time crash risk prediction model using decision tree method and adaptive neural network fuzzy inference system (ANFIS). By comparing several real-time crash risk prediction methods, it was found that our proposed method had higher precision than others. And the training error and testing error were minimum (0.280 and 0.291, resp.) when the data during 0 to 30 minutes prior to crash occurrence was collected and the decision tree-ANFIS method was applied to train and establish the real-time crash risk prediction model. The prediction accuracy of the crash occurrence could reach 65% when 0.60 was considered as the crash prediction threshold.

scholarly journals Outlier Detection in Wireless Sensor Networks Data by Entropy Based K-NN Predictor

2019 ◽  
Vol 8 (12) ◽  
pp. 5483-5489
Keyword(s):  
Prediction Model ◽  
Real Time ◽  
Outlier Detection ◽  
Window Size ◽  
Environmental Parameters ◽  
Sensor Nodes ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Training Dataset ◽  
Time Motion

Anomaly (outlier) detection is plays very significant role in ESN based monitoring application using on large data used for biomedical and defence. Wireless Sensor network monitor environmental parameters (temperature, humidity, pressure, vibration etc). Group of sensor nodes forms a (WSN) and observations collected from these sensor produces low data quality and reliability due to the limited energy, memory, computation capability and bandwidth. The dynamic environment of network and roughness of the working condition are also responsible to generate inaccuracy in measurements. In this paper, an approach for outliers detection based entropy value of received sensor voltages is applied using KNN prediction model .The algorithm development and analysis involves a real time database generated on 14 sets of MICA2 wireless sensor kit with anomaly inserted by real time motion based intrusion in the lab by volunteers from Intel Berkeley lab. On each sensor data pair segmentation is applied by fixed window size in order get large outliers’ measurements training dataset. The analysis demonstrates the measurement accuracy in detection of number of outliers that its 86%. Moreover, the algorithm also provides an analysis in terms of impact of variation in distance types and number of nearest neighbours in the KNN prediction model. This work is helpful in the application in the situations where high amount of noise or distortions are present. The outlier part from distorted data can be figured out and recollected to enhance application accuracy.

A Control System of Killing Oxygen by Adding Aluminum Process

2013 ◽  
Vol 303-306 ◽  
pp. 1141-1144
Author(s):  
Guo Nian Chen ◽  
Kang Ling Fang ◽  
Xiao Li
Keyword(s):  
Control System ◽  
Prediction Model ◽  
Observational Data ◽  
Hybrid Model ◽  
Optimization Method ◽  
Signal Separation ◽  
Practical Application ◽  
Data Sequence ◽  
The Core ◽  
Rolling Optimization

A kind of control system of killing oxygen by adding aluminum process is described in this thesis. The prediction model Alcan amount is the core of the process. In this system, the signal separation and the rolling optimization method based on observational data sequence given oxygen Alcan hybrid model for online. The practical application has achieved good results

scholarly journals Performance Analysis of IoT-Based Sensor, Big Data Processing, and Machine Learning Model for Real-Time Monitoring System in Automotive Manufacturing

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2946 ◽  
Author(s):  
Muhammad Syafrudin ◽  
Ganjar Alfian ◽  
Norma Fitriyani ◽  
Jongtae Rhee
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Processing ◽  
Prediction Model ◽  
Real Time ◽  
Manufacturing Process ◽  
Sensor Data ◽  
Big Data Processing ◽  
Automotive Manufacturing ◽  
Hybrid Prediction

With the increase in the amount of data captured during the manufacturing process, monitoring systems are becoming important factors in decision making for management. Current technologies such as Internet of Things (IoT)-based sensors can be considered a solution to provide efficient monitoring of the manufacturing process. In this study, a real-time monitoring system that utilizes IoT-based sensors, big data processing, and a hybrid prediction model is proposed. Firstly, an IoT-based sensor that collects temperature, humidity, accelerometer, and gyroscope data was developed. The characteristics of IoT-generated sensor data from the manufacturing process are: real-time, large amounts, and unstructured type. The proposed big data processing platform utilizes Apache Kafka as a message queue, Apache Storm as a real-time processing engine and MongoDB to store the sensor data from the manufacturing process. Secondly, for the proposed hybrid prediction model, Density-Based Spatial Clustering of Applications with Noise (DBSCAN)-based outlier detection and Random Forest classification were used to remove outlier sensor data and provide fault detection during the manufacturing process, respectively. The proposed model was evaluated and tested at an automotive manufacturing assembly line in Korea. The results showed that IoT-based sensors and the proposed big data processing system are sufficiently efficient to monitor the manufacturing process. Furthermore, the proposed hybrid prediction model has better fault prediction accuracy than other models given the sensor data as input. The proposed system is expected to support management by improving decision-making and will help prevent unexpected losses caused by faults during the manufacturing process.

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