Sequencing of chillers by estimating chiller power consumption using artificial neural networks

2007 ◽  
Vol 42 (1) ◽  
pp. 180-188 ◽  
Author(s):  
Yung-Chung Chang
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Power Consumption ◽  
Artificial Neural

scholarly journals Integrated approach to the planning of energy consumption by non-traction railway consumers

2018 ◽  
Vol 239 ◽  
pp. 01056 ◽  
Author(s):  
Alexander Galkin ◽  
Alexey Kovalev ◽  
Timur Shayuhov
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Correlation Analysis ◽  
Power Consumption ◽  
Systematic Approach ◽  
Integrated Approach ◽  
Expert Assessments ◽  
The Subject ◽  
Artificial Neural ◽  
High Degree

Non-traction railway load consumes a significant amount of electricity. Russian Railways supplies electricity not only to its structural units but also to other consumers. Private houses, individual entrepreneurs and small production facilities located near the railway are powered by the RZD. It is very important for the company to plan consumption for non- traction needs. The subject of the study in the paper is a systematic approach to the planning of power consumption using the mathematical apparatus of artificial neural networks, correlation analysis and the method of expert assessments. The method of expert assessments allows identifying the most significant factors that affect the consumption of electricity. It is necessary to attract experienced professionals in the field of electricity, working at a particular enterprise. They are able to determine with a high degree of accuracy those factors that have a significant impact on the consumption of the organization. Correlation analysis allows you to mathematically check the degree of influence of a single factor on the resulting value. The apparatus of artificial neural networks allows building a forecast of power consumption, taking into account the influence of external factors. The authors propose to use a systematic approach to the planning of power consumption. It is necessary to combine three tools: the method of expert assessments, correlation analysis and artificial neural networks. The combination of these tools will improve the accuracy of power consumption planning and, as a result, will lead to increased economic efficiency due to the rational consumption of electricity.

scholarly journals Estimated electric power consumption by means of artificial neural networks and autoregressive models with exogenous input methods

2013 ◽  
Vol 8 (14) ◽  
pp. 585-592 ◽  
Author(s):  
L Rojas Renteriacute a J ◽  
Luna Rubio R ◽  
L Gonzaacute lez Peacute rez J ◽  
A Gonzaacute lez Gutieacute rrez C ◽  
Rojas Molina A ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Power Consumption ◽  
Electric Power ◽  
Autoregressive Models ◽  
Electric Power Consumption ◽  
Artificial Neural

scholarly journals Forecasting the electric consumption of objects using artificial neural networks

2020 ◽  
Vol 216 ◽  
pp. 01170
Author(s):  
F A. Hoshimov ◽  
I I Bakhadirov ◽  
A A. Alimov ◽  
M T Erejepov
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Power Consumption ◽  
Artificial Neural

The possibility of using artificial neural networks of the Matlab mathematical package for predicting the power consumption of objects is considered, the parameters that affect the power consumption are studied.

FORECASTING ELECTRIC CONSUMPTION OF THE ENTERPRISE USING ARTIFICIAL NEURAL NETWORKS

2021 ◽  
Vol 7 (1) ◽  
pp. 177-193
Author(s):  
Sameh A. Kassem ◽  
Abdulla H. A. EBRAHIM ◽  
Abdulla M. Khasan ◽  
Alla G. Logacheva
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Energy Consumption ◽  
Power Consumption ◽  
Data Driven ◽  
Past Century ◽  
Medium Term ◽  
The Past ◽  
Artificial Neural ◽  
Feedback Algorithm

Energy consumption has increased dramatically over the past century due to many factors, including both technological, social and economic factors. Therefore, predicting energy consumption is of great importance for many parameters, including planning, management, optimization and conservation. Data-driven models for predicting energy consumption have grown significantly over the past several decades due to their improved performance, reliability, and ease of deployment. Artificial neural networks are among the most popular data-driven approaches among the many different types of models today. This article discusses the possibility of using artificial neural networks for medium-term forecasting of the power consumption of an enterprise. The task of constructing an artificial neural network using a feedback algorithm for training a network based on the Matlab mathematical package has been implemented. The authors have analyzed such characteristics as parameter setting, implementation complexity, learning rate, convergence of the result, forecasting accuracy, and stability. The results obtained led to the conclusion that the feedback algorithm is well suited for medium-term forecasting of power consumption.

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