Abstract
In this paper, we propose a new load forecasting method for smart air conditioning (A/C) based on the modified thermodynamics of indoor temperature and the unbiased finite memory estimator (UFME). Based on modified first-order thermodynamics, the dynamic behavior of indoor temperature can be described by the time-domain state-space model, and an accurate estimate of indoor temperature can be achieved by the proposed UFME. In addition, a reliable A/C load forecast can be obtained using the proposed method. Our study involves the experimental validation of the proposed A/C load forecasting method and communication construction between DR server and HEMS in a test bed. Through experimental data sets, the effectiveness of the proposed estimation method is validated.
| Original language | English |
|---|---|
| Pages (from-to) | 559-567 |
| Number of pages | 9 |
| Journal | Journal of Electrical Engineering and Technology |
| Volume | 13 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 2018 Mar 1 |
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Keywords
- Air condition(A/C)
- Demand response(DR)
- Home energy management system(HEMS)
- Thermodynamic model
- Unbiased finite memory estimation (UFME)
ASJC Scopus subject areas
- Electrical and Electronic Engineering
Cite this
Smart air condition load forecasting based on thermal dynamic model and finite memory estimation for peak-energy distribution. / Choi, Hyun Duck; Lee, Soon Woo; Pae, Dong Sung; You, Sung Hyun; Lim, Myo Taeg.
In: Journal of Electrical Engineering and Technology, Vol. 13, No. 2, 01.03.2018, p. 559-567.Research output: Contribution to journal › Article
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TY - JOUR
T1 - Smart air condition load forecasting based on thermal dynamic model and finite memory estimation for peak-energy distribution
AU - Choi, Hyun Duck
AU - Lee, Soon Woo
AU - Pae, Dong Sung
AU - You, Sung Hyun
AU - Lim, Myo Taeg
PY - 2018/3/1
Y1 - 2018/3/1
N2 - In this paper, we propose a new load forecasting method for smart air conditioning (A/C) based on the modified thermodynamics of indoor temperature and the unbiased finite memory estimator (UFME). Based on modified first-order thermodynamics, the dynamic behavior of indoor temperature can be described by the time-domain state-space model, and an accurate estimate of indoor temperature can be achieved by the proposed UFME. In addition, a reliable A/C load forecast can be obtained using the proposed method. Our study involves the experimental validation of the proposed A/C load forecasting method and communication construction between DR server and HEMS in a test bed. Through experimental data sets, the effectiveness of the proposed estimation method is validated.
AB - In this paper, we propose a new load forecasting method for smart air conditioning (A/C) based on the modified thermodynamics of indoor temperature and the unbiased finite memory estimator (UFME). Based on modified first-order thermodynamics, the dynamic behavior of indoor temperature can be described by the time-domain state-space model, and an accurate estimate of indoor temperature can be achieved by the proposed UFME. In addition, a reliable A/C load forecast can be obtained using the proposed method. Our study involves the experimental validation of the proposed A/C load forecasting method and communication construction between DR server and HEMS in a test bed. Through experimental data sets, the effectiveness of the proposed estimation method is validated.
KW - Air condition(A/C)
KW - Demand response(DR)
KW - Home energy management system(HEMS)
KW - Thermodynamic model
KW - Unbiased finite memory estimation (UFME)
UR - http://www.scopus.com/inward/record.url?scp=85041810577&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85041810577&partnerID=8YFLogxK
U2 - 10.5370/JEET.2018.13.2.559
DO - 10.5370/JEET.2018.13.2.559
M3 - Article
AN - SCOPUS:85041810577
VL - 13
SP - 559
EP - 567
JO - Journal of Electrical Engineering and Technology
JF - Journal of Electrical Engineering and Technology
SN - 1975-0102
IS - 2
ER -