scholarly journals Short-Term Energy Monitoring (STEM): Application of the PSTAR method to a residence in Fredericksburg, Virginia

1988 ◽  
Author(s):  
K. Subbarao ◽  
J.D. Burch ◽  
C.E. Hancock ◽  
A. Lekov ◽  
J.D. Balcomb
Keyword(s):  
Short Term ◽  
Energy Monitoring ◽  
Term Energy

scholarly journals Heating Performance Analysis for Short-Term Energy Monitoring and Prediction Using Multi-Family Residential Energy Consumption Data

Energies ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 3189
Author(s):  
Sukjoon Oh ◽  
Chul Kim ◽  
Joonghyeok Heo ◽  
Sung Lok Do ◽  
Kee Han Kim
Keyword(s):  
Performance Analysis ◽  
Change Point ◽  
Energy Use ◽  
Residential Buildings ◽  
Interval Data ◽  
Short Term ◽  
Smart Meters ◽  
Energy Monitoring ◽  
Heating Performance ◽  
Term Energy

Many smart apartments and renovated residential buildings have installed Smart Meters (SMs), which collect interval data to accelerate more efficient energy management in multi-family residential buildings. SMs are widely used for electricity, but many utility companies have been working on systems for natural gas and water monitoring to be included in SMs. In this study, we analyze heating energy use data obtained from SMs for short-term monitoring and annual predictions using change-point models for the coefficient checking method. It was found that 9-month periods were required to search the best short-term heating energy monitoring periods when non-weather-related and weather-related heating loads and heating change-point temperatures are considered. In addition, the 9-month to 11-month periods were needed for the analysis to apply to other case study residences in the same high-rise apartment. For the accurate annual heating prediction, 11-month periods were necessary. Finally, the results from the heating performance analysis of this study were compared with the cooling performance analysis from a previous study. This study found that the coefficient checking method is a simple and easy-to-interpret approach to analyze interval heating energy use in multi-family residential buildings. It was also found that the period of short-term energy monitoring should be carefully selected to effectively collect targeted heating and cooling data for an energy audit or annual prediction.

Short term energy monitoring: a road to long term energy savings?

Facilities ◽  
2002 ◽  
Vol 20 (10) ◽  
pp. 303-313 ◽  
Author(s):  
John A. Bryant ◽  
Kimberly Carlson
Keyword(s):  
Energy Savings ◽  
Short Term ◽  
Energy Monitoring ◽  
Term Energy

scholarly journals Ensemble Prediction Approach Based on Learning to Statistical Model for Efficient Building Energy Consumption Management

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 405
Author(s):  
Anam Nawaz Khan ◽  
Naeem Iqbal ◽  
Rashid Ahmad ◽  
Do-Hyeun Kim
Keyword(s):  
Energy Consumption ◽  
Statistical Model ◽  
Residential Buildings ◽  
Residential Building ◽  
Experimental Results ◽  
Consumption Patterns ◽  
Ensemble Prediction ◽  
Short Term ◽  
Term Energy ◽  
Prediction Approach

With the development of modern power systems (smart grid), energy consumption prediction becomes an essential aspect of resource planning and operations. In the last few decades, industrial and commercial buildings have thoroughly been investigated for consumption patterns. However, due to the unavailability of data, the residential buildings could not get much attention. During the last few years, many solutions have been devised for predicting electric consumption; however, it remains a challenging task due to the dynamic nature of residential consumption patterns. Therefore, a more robust solution is required to improve the model performance and achieve a better prediction accuracy. This paper presents an ensemble approach based on learning to a statistical model to predict the short-term energy consumption of a multifamily residential building. Our proposed approach utilizes Long Short-Term Memory (LSTM) and Kalman Filter (KF) to build an ensemble prediction model to predict short term energy demands of multifamily residential buildings. The proposed approach uses real energy data acquired from the multifamily residential building, South Korea. Different statistical measures are used, such as mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and R2 score, to evaluate the performance of the proposed approach and compare it with existing models. The experimental results reveal that the proposed approach predicts accurately and outperforms the existing models. Furthermore, a comparative analysis is performed to evaluate and compare the proposed model with conventional machine learning models. The experimental results show the effectiveness and significance of the proposed approach compared to existing energy prediction models. The proposed approach will support energy management to effectively plan and manage the energy supply and demands of multifamily residential buildings.

Lack of Effect of Exercise Time of Day on Acute Energy Intake in Healthy Men

2010 ◽  
Vol 20 (4) ◽  
pp. 350-356 ◽  
Author(s):  
Katriona J.M O’Donoghue ◽  
Paul A. Fournier ◽  
Kym J. Guelfi
Keyword(s):  
Energy Intake ◽  
Time Of Day ◽  
Treadmill Running ◽  
Short Term ◽  
Exercise Time ◽  
Acute Bout ◽  
Healthy Men ◽  
Significant Difference ◽  
Term Energy ◽  
Successful Weight Loss

Although the manipulation of exercise and dietary intake to achieve successful weight loss has been extensively studied, it is unclear how the time of day that exercise is performed may affect subsequent energy intake. The purpose of the current study was to investigate the effect of an acute bout of exercise performed in the morning compared with an equivalent bout of exercise performed in the afternoon on short-term energy intake. Nine healthy male participants completed 3 trials: morning exercise (AM), afternoon exercise (PM), or control (no exercise; CON) in a randomized counterbalanced design. Exercise consisted of 45 min of treadmill running at 75% VO2peak. Energy intake was assessed over a 26-hr period with the participants eating ad libitum from a standard assortment of food items of known quantity and composition. There was no significant difference in overall energy intake (M ± SD; CON 23,505 ± 6,938 kJ, AM 24,957 ± 5,607 kJ, PM 24,560 ± 5,988 kJ; p = .590) or macronutrient preferences during the 26-hr period examined between trials. Likewise, no differences in energy intake or macronutrient preferences were observed at any of the specific individual meal periods examined (i.e., breakfast, lunch, dinner) between trials. These results suggest that the time of day that exercise is performed does not significantly affect short-term energy intake in healthy men.

scholarly journals Short-Term Energy Balancing With Increasing Levels of Wind Energy

2012 ◽  
Vol 3 (4) ◽  
pp. 769-776 ◽  
Author(s):  
Juha Kiviluoma ◽  
Peter Meibom ◽  
Aidan Tuohy ◽  
Niamh Troy ◽  
Michael Milligan ◽  
...  
Keyword(s):  
Wind Energy ◽  
Energy Balancing ◽  
Short Term ◽  
Term Energy
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