Description

Commercial buildings alone are responsible for 36% of the total United States electricity consumption, and 30% of this electricity consumption is wasted on average. One of the greatest challenges in improving building energy efficiency lies in the ability to do simple and non-intrusive load disaggregation. In this paper, we propose a novel unsupervised, non-intrusive building energy disaggregation technique using only 15-minute interval whole-building energy consumption and weather data. The proposed disaggregation technique consists of an analysis loop with three steps. Step 1 analyzes the change in energy consumption in 15-minute intervals for every 3°C temperature range to identify statistically significant temperature dependent and independent the equipment turn on-off states. In step 2, a probabilistic approach is developed using a Factorial Hidden Markov Model (FHMM) that combines equipment state transition matrices from the equipment turn on-off states of step 1 and an emission matrix representing all possible combinations of equipment energy consumption in each state. In step 3, FHMM identifies the best possible combinations of the equipment state and consumption for every point in time using an Expectation Maximization (EM) algorithm. Ultimately, specific equipment such as HVAC, plug load, and lighting, can be identified virtually without setting foot in the building. The proposed disaggregation technique is validated by comparing real-world, sub-metered building equipment energy consumption data to the whole building data. The results show that the method achieves at least 80% accuracy. Using the disaggregation technique to identify the breakdown of HVAC, lighting, and plug load, we compare the results from building population studies to identify potential areas of energy waste and generate an automated virtual energy audit report. The report provides personalized building recommendations such as improving HVAC operations, lighting, and equipment scheduling, including a measure of potential return-on-investment.

Citation: 2017 Annual Conference, Long Beach, CA, Conference Papers

Product Details

Published:

2017

Number of Pages:

8

Units of Measure:

Dual

File Size:

1 file , 2.5 MB

Product Code(s):

D-LB-17-C052