A hybrid teaching-learning artificial neural network for building electrical energy consumption prediction

2018 ◽  
Vol 174 ◽  
pp. 323-334 ◽  
Author(s):  
Kangji Li ◽  
Xianming Xie ◽  
Wenping Xue ◽  
Xiaoli Dai ◽  
Xu Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Energy Consumption ◽  
Electrical Energy ◽  
Electrical Energy Consumption ◽  
Hybrid Teaching ◽  
Artificial Neural ◽  
Energy Consumption Prediction ◽  
Teaching Learning ◽  
Consumption Prediction

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.

An Artificial Neural Network in Short-Term Electrical Load Forecasting of a University Campus: A Case Study

2012 ◽  
Author(s):  
David Palchak ◽  
Siddharth Suryanarayanan ◽  
Daniel Zimmerle
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Energy Consumption ◽  
Electrical Energy ◽  
Time Of Day ◽  
University Campus ◽  
Electrical Load ◽  
Ann Model ◽  
Short Term ◽  
Artificial Neural

This paper presents an artificial neural network (ANN) for forecasting the short-term electrical load of a university campus using real historical data from Colorado State University. A spatio-temporal ANN model with multiple weather variables as well as time identifiers, such as day of week and time of day, are used as inputs to the network presented. The choice of the number of hidden neurons in the network is made using statistical information and taking into account the point of diminishing returns. The performance of this ANN is quantified using three error metrics: the mean average percent error (MAPE); the error in the ability to predict the occurrence of the daily peak hour; and the difference in electrical energy consumption between the predicted and the actual values in a 24-hour period. These error measures provide a good indication of the constraints and applicability of these predictions. In the presence of some enabling technologies such as energy storage, rescheduling of non-critical loads, and availability of time of use (ToU) pricing, the possible DSM options that could stem from an accurate prediction of energy consumption of a campus include the identification of anomalous events as well the management of usage.

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.

Mid-Term Energy Consumption Prediction of Crude Oil Pipeline Pump Unit Based on GSCV-SVM

2020 ◽  
Author(s):  
Lei Xu ◽  
Lei Hou ◽  
Yu Li ◽  
Zhenyu Zhu ◽  
Jiaquan Liu ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Crude Oil ◽  
Electrical Energy ◽  
Electrical Energy Consumption ◽  
Pump Unit ◽  
Test Set ◽  
Oil Pipeline ◽  
Oil Pipelines ◽  
Energy Consumption Prediction ◽  
Consumption Prediction

Abstract Energy consumption prediction plays an important role in pipeline operation regulation and energy management. Accurate energy consumption prediction is helpful to make important decisions, including unit commitment, batch scheduling, load dispatching, energy consumption target setting, etc. The energy consumption of crude oil pipeline is mainly the electrical energy of pump unit. The average annual electrical energy consumption of China’s crude oil pipelines accounts for more than half of the annual operating cost of pipelines. Therefore, the prediction of electrical energy consumption of crude oil pipelines is critical. The energy consumption prediction of crude oil pipelines is very complicated. Firstly, it depends on the variables related to operation parameter, crude oil physical property parameter, environmental parameter and equipment parameter. Secondly, its nonlinearity is strong. Thirdly, the available samples are too little. Through the study on the monthly operation data collected by the Supervisory Control And Data Acquisition (SCADA) system and energy consumption analysis, the turnover and the electrical energy consumption is selected as input variable and output variable, respectively. The support vector machines (SVM) is introduced to predict the monthly electric energy consumption of crude oil pipelines driving oil pumps. However, the generalization capability of SVM is highly dependent on appropriate parameter setting, such as penalty coefficient and kernel parameter. The selection of the optimal parameters is critical to achieving good performance in the learning process. Therefore, in order to improve the generalization ability, GridSearchCV was adopted to optimize the hyperparameters of SVM. Taking a crude oil pipeline from Qinhuangdao City, Hebei Province to Fangshan District, Beijing as an example, the actual operation data for four consecutive years (48 months) are used for this study. The data are divided into training set and test set by stratified sampling method, which consist of 28 samples and 20 samples respectively. The mean absolute percentage error (MAPE), root mean square error (RMSE), mean absolute error (MAE) and coefficient of determination (R2) on the test set are 3.42, 21.64, 14.31 and 0.94 respectively. Compared with other five state-of-the-art prediction methods in predictive accuracy, the result shows that GSCV-SVM has the best performance in the case of small samples, and the prediction results are in good agreement with the actual data.

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