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.

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.

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

Enabling local authorities to produce short-term energy plans

2016 ◽  
Vol 27 (2) ◽  
pp. 146-166 ◽  
Author(s):  
Stella Androulaki ◽  
Haris Doukas ◽  
Vangelis Marinakis ◽  
Leandro Madrazo ◽  
Nikoletta-Zabbeta Legaki
Keyword(s):  
Decision Support ◽  
Energy Use ◽  
Energy Optimization ◽  
Data Sources ◽  
Multiple Sources ◽  
Short Term ◽  
Content Type ◽  
Related Data ◽  
Multidisciplinary Data ◽  
Term Energy

Purpose – The purpose of this paper is to identify the most appropriate multidisciplinary data sources related with energy optimization decision support as well as the related methodologies, tools and techniques for data capturing and processing for each of them. Design/methodology/approach – A review is conducted on the state-of-play of decision support systems for energy optimization, focussing on the municipal sector, followed by an identification of the most appropriate multidisciplinary data sources related with energy optimization decision support. An innovative methodology is outlined to integrate semantically modeled data from multiple sources, to assist city authorities in energy management. Findings – City authorities need to lead relevant actions toward energy-efficient neighborhoods. Although there are more and more energy and other related data available at the city level, there are no established methods and tools integrating and analyzing them in a smart way, with the purpose to support the decision-making process on energy use optimization. Originality/value – A novel multidimensional approach is proposed, using semantic technologies to integrate data from multiple sources, to assist city authorities to produce short-term energy plans in an integrated, transparent and comprehensive way.

scholarly journals Energy Conservation, Efficiency and Demand Side Management

2012 ◽  
pp. 294-299
Author(s):  
Pappu Kumar Singh ◽  
A. K. Mahapatra ◽  
U. Prasad
Keyword(s):  
Energy Conservation ◽  
Energy Security ◽  
Energy Use ◽  
Economic Security ◽  
Energy Sources ◽  
Short Term ◽  
Term Energy ◽  
Conservation Efficiency ◽  
Efficient Energy Use

The efficient use of energy is the key to maintaining our world’s resources; indeed our future depends on it. Energy conservation can be achieved through increased efficient energy use, in connection with decreased energy consumption and reduced consumption from conventional energy sources. Energy conservation can result in increased financial capital, environmental quality, national security, personal security, and human comfort. Individuals and organizations that are direct consumers of energy choose to conserve energy to reduce energy costs and promote economic security. In view of the nation's energy security interests, it is important to be increasing alternative fuel capability throughout the fleet. The need to ensure the nation's long-term energy security is of such vital concern that it takes precedent over possible short-term convention energy sources consumption and environmental impacts.

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
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