scholarly journals Artificial Neural Network-Based Residential Energy Consumption Prediction Models Considering Residential Building Information and User Features in South Korea

2019 ◽  
Vol 12 (1) ◽  
pp. 109 ◽  
Author(s):  
Mansu Kim ◽  
Sungwon Jung ◽  
Joo-won Kang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Regression Analysis ◽  
Energy Consumption ◽  
Prediction Models ◽  
Residential Buildings ◽  
Residential Energy ◽  
Artificial Neural ◽  
Energy Consumption Prediction ◽  
Consumption Prediction

When researching the energy consumption of residential buildings, it is becoming increasingly important to consider how residents use energy. With the advancement of computing power and data analysis techniques, it is now possible to analyze user information using big data techniques. Here, we endeavored to integrate user information with the physical characteristics of residential buildings to analyze how these elements impact energy consumption. Regression analysis was conducted to accurately identify the impact of each element on energy consumption. It was found that six elements were influential in all seasons: the number of exterior walls, housing direction, housing area, number of years occupied, number of household members, and the occupation of the household head. The elements that had an impact in each period were then derived. Based on the results of the regression analysis, input variables for the training of an artificial neural network (ANN) model were selected for each period, and residential energy consumption prediction models were implemented based on actual consumption. The elements identified as those affecting energy consumption, through regression analysis, can be used for implementing prediction models with advanced forms. This study is significant in that we derived influential elements from an integrative perspective.

scholarly journals Using Hybrid Machine Learning Methods to Predict and Improve the Energy Consumption Efficiency in Oil and Gas Fields

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Jun Li ◽  
Yidong Guo ◽  
Xiangyang Zhang ◽  
Zhanbao Fu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Energy Consumption ◽  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Energy Usage ◽  
Gas Industry ◽  
Learning Machine ◽  
Energy Consumption Prediction ◽  
Consumption Prediction

Oil and gas will remain essential to global economic development and prosperity for decades to come, and the oil and gas industry is an energy-intensive industry. Thus, enhancing energy efficiency for producing oil and gas in oil and gas companies is an important issue. The intelligent energy consumption prediction method with the ability to analyze energy consumption patterns and to identify targets for energy saving proved itself as an effective approach for energy efficiency in many industrial domains. Moreover, prediction of energy consumption enables managers to scientifically plan out the energy usage of energy production and to shift energy usage to off-peak periods. However, it still remains a challenging issue to some degree with the unpredictability and uncertainty caused by various energy consumption behaviors, and this phenomenon is becoming more obvious in the oil and gas company. To this end, in our work, we primarily discussed the forecasting of the energy consumption in the oil and gas company. Firstly, four different forecasting models, support vector machine, linear regression, extreme learning machine, and artificial neural network, were trained on the training dataset and then evaluated by the test dataset. Secondly, in order to enhance the energy consumption prediction accuracy, the combinations of all these four models were examined with the RMSE value by taking the average of two models’ outputs. The outcomes show that these four different models are able to predict energy consumption with good accuracy, but the hybrid model—artificial neural network and extreme learning machine—would present higher accuracy. In addition, the hybrid model is installed in the energy management system of the oil and gas industry to manage oil field energy consumption and improve the efficiency.

scholarly journals Corrosion Behavior of LENS Deposited CoCrMo Alloy Using Bayesian Regularization-Based Artificial Neural Network (BRANN)

2021 ◽  
Vol 7 (3) ◽  
Author(s):  
Nagoor Basha Shaik ◽  
Kedar Mallik Mantrala ◽  
Balaji Bakthavatchalam ◽  
Qandeel Fatima Gillani ◽  
M. Faisal Rehman ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Corrosion Rate ◽  
Prediction Models ◽  
Test Time ◽  
Bayesian Regularization ◽  
Cobalt Chromium ◽  
Corrosion Properties ◽  
Cocrmo Alloy ◽  
Artificial Neural

AbstractThe well-known fact of metallurgy is that the lifetime of a metal structure depends on the material's corrosion rate. Therefore, applying an appropriate prediction of corrosion process for the manufactured metals or alloys trigger an extended life of the product. At present, the current prediction models for additive manufactured alloys are either complicated or built on a restricted basis towards corrosion depletion. This paper presents a novel approach to estimate the corrosion rate and corrosion potential prediction by considering significant major parameters such as solution time, aging time, aging temperature, and corrosion test time. The Laser Engineered Net Shaping (LENS), which is an additive manufacturing process used in the manufacturing of health care equipment, was investigated in the present research. All the accumulated information used to manufacture the LENS-based Cobalt-Chromium-Molybdenum (CoCrMo) alloy was considered from previous literature. They enabled to create a robust Bayesian Regularization (BR)-based Artificial Neural Network (ANN) in order to predict with accuracy the material best corrosion properties. The achieved data were validated by investigating its experimental behavior. It was found a very good agreement between the predicted values generated with the BRANN model and experimental values. The robustness of the proposed approach allows to implement the manufactured materials successfully in the biomedical implants.

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