scholarly journals Energy Consumption Prediction Analysis for the Retrofitting of an Urban Area using Artificial Neural Networks

2015 ◽  
Vol 4 (12) ◽  
pp. 1454-1457
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Energy Consumption ◽  
Urban Area ◽  
Prediction Analysis ◽  
Artificial Neural ◽  
Energy Consumption Prediction ◽  
Consumption Prediction

Building Energy Prediction Using Artificial Neural Networks (LSTM)

2020 ◽  
Author(s):  
Sankhanil Goswami
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Energy Consumption ◽  
Energy Use ◽  
Prediction Models ◽  
Short Term Memory ◽  
Significant Proportion ◽  
Cooling Load ◽  
Energy Prediction ◽  
Artificial Neural

Abstract Modern buildings account for a significant proportion of global energy consumption worldwide. Therefore, accurate energy use forecast is necessary for energy management and conservation. With the advent of smart sensors, a large amount of accurate energy data is available. Also, with the advancements in data analytics and machine learning, there have been numerous studies on developing data-driven prediction models based on Artificial Neural Networks (ANNs). In this work a type of ANN called Large Short-Term Memory (LSTM) is used to predict the energy use and cooling load of an existing building. A university administrative building was chosen for its typical commercial environment. The network was trained with one year of data and was used to predict the energy consumption and cooling load of the following year. The mean absolute testing error for the energy consumption and the cooling load were 0.105 and 0.05. The percentage mean accuracy was found to be 92.8% and 96.1%. The process was applied to several other buildings in the university and similar results were obtained. This indicates the model can successfully predict the energy consumption and cooling load for the buildings studied. The further improvement and application of this technique for optimizing building performance are also explored.

Deploying Arima and Artificial Neural Networks Models to Predict Energy Consumption in Taiwan

2013 ◽  
Vol 11 (12) ◽  
pp. 2333-2340 ◽  
Author(s):  
Feng-Kuang Chuang ◽  
Chih-Young Hung ◽  
Chi-Ya Chang ◽  
Kuo-Cheng Kuo
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Energy Consumption ◽  
Artificial Neural

scholarly journals Modeling Cutting Forces in High-Speed Turning using Artificial Neural Networks

TecnoLógicas ◽  
2021 ◽  
Vol 24 (51) ◽  
pp. e1671
Author(s):  
Luis W. Hernández-González ◽  
Dagnier A. Curra-Sosa ◽  
Roberto Pérez-Rodríguez ◽  
Patricia D.C. Zambrano-Robledo
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Energy Consumption ◽  
Cutting Forces ◽  
High Speed ◽  
Cutting Tool ◽  
Cutting Speed ◽  
Machining Time ◽  
High Speed Turning ◽  
Artificial Neural

Cutting forces are very important variables in machining performance because they affect surface roughness, cutting tool life, and energy consumption. Reducing electrical energy consumption in manufacturing processes not only provides economic benefits to manufacturers but also improves their environmental performance. Many factors, such as cutting tool material, cutting speed, and machining time, have an impact on cutting forces and energy consumption. Recently, many studies have investigated the energy consumption of machine tools; however, only a few have examined high-speed turning of plain carbon steel. This paper seeks to analyze the effects of cutting tool materials and cutting speed on cutting forces and Specific Energy Consumption (SEC) during dry high-speed turning of AISI 1045 steel. For this purpose, cutting forces were experimentally measured and compared with estimates of predictive models developed using polynomial regression and artificial neural networks. The resulting models were evaluated based on two performance metrics: coefficient of determination and root mean square error. According to the results, the polynomial models did not reach 70 % in the representation of the variability of the data. The cutting speed and machining time associated with the highest and lowest SEC of CT5015-P10 and GC4225-P25 inserts were calculated. The lowest SEC values of these cutting tools were obtained at a medium cutting speed. Also, the SEC of the GC4225 insert was found to be higher than that of the CT5015 tool.

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