Abstract
With increases in global energy demand and the rapid consumption of fossil fuels, the use of green energy and more efficient energy management approaches are receiving serious attention. Our focus is on improving energy resource scheduling in smart buildings and homes to minimize cost, while meeting energy demand. Here, we present an approach using Action Dependent Heuristic Dynamic Programming (ADHDP) optimization for a smart home set-up using solar panels, wind turbines, and a storage battery.In this work, we trained and evaluated our ADHDP approach using different simulation scenarios with various amounts of available renewable energy. We then demonstrated via computer simulation that our approach is more effective in cost minimization compared to a standard rule-based method. A correlation between optimization improvement and available renewable energy was also confirmed by computer simulation in all scenarios.
| Original language | English |
|---|---|
| Title of host publication | 2018 IEEE International Smart Cities Conference, ISC2 2018 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9781538659595 |
| DOIs | |
| Publication status | Published - Feb 28 2019 |
| Event | 2018 IEEE International Smart Cities Conference, ISC2 2018 - Kansas City, United States Duration: Sep 16 2018 → Sep 19 2018 |
Publication series
| Name | 2018 IEEE International Smart Cities Conference, ISC2 2018 |
|---|
Conference
| Conference | 2018 IEEE International Smart Cities Conference, ISC2 2018 |
|---|---|
| Country | United States |
| City | Kansas City |
| Period | 9/16/18 → 9/19/18 |
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Keywords
- Action Dependent Heuristic Dynamic Programming
- Energy Resource Scheduling
- Neural Dynamic Programming
- Renewable Energy
- Smart Buildings
- Smart Homes
ASJC Scopus subject areas
- Urban Studies
- Computer Science Applications
- Computer Networks and Communications
Cite this
Renewable Energy Management Using Action Dependent Heuristic Dynamic Programming. / Sterling, Gulnaz; Tyler, Benjamin.
2018 IEEE International Smart Cities Conference, ISC2 2018. Institute of Electrical and Electronics Engineers Inc., 2019. 8656942 (2018 IEEE International Smart Cities Conference, ISC2 2018).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Renewable Energy Management Using Action Dependent Heuristic Dynamic Programming
AU - Sterling, Gulnaz
AU - Tyler, Benjamin
PY - 2019/2/28
Y1 - 2019/2/28
N2 - With increases in global energy demand and the rapid consumption of fossil fuels, the use of green energy and more efficient energy management approaches are receiving serious attention. Our focus is on improving energy resource scheduling in smart buildings and homes to minimize cost, while meeting energy demand. Here, we present an approach using Action Dependent Heuristic Dynamic Programming (ADHDP) optimization for a smart home set-up using solar panels, wind turbines, and a storage battery.In this work, we trained and evaluated our ADHDP approach using different simulation scenarios with various amounts of available renewable energy. We then demonstrated via computer simulation that our approach is more effective in cost minimization compared to a standard rule-based method. A correlation between optimization improvement and available renewable energy was also confirmed by computer simulation in all scenarios.
AB - With increases in global energy demand and the rapid consumption of fossil fuels, the use of green energy and more efficient energy management approaches are receiving serious attention. Our focus is on improving energy resource scheduling in smart buildings and homes to minimize cost, while meeting energy demand. Here, we present an approach using Action Dependent Heuristic Dynamic Programming (ADHDP) optimization for a smart home set-up using solar panels, wind turbines, and a storage battery.In this work, we trained and evaluated our ADHDP approach using different simulation scenarios with various amounts of available renewable energy. We then demonstrated via computer simulation that our approach is more effective in cost minimization compared to a standard rule-based method. A correlation between optimization improvement and available renewable energy was also confirmed by computer simulation in all scenarios.
KW - Action Dependent Heuristic Dynamic Programming
KW - Energy Resource Scheduling
KW - Neural Dynamic Programming
KW - Renewable Energy
KW - Smart Buildings
KW - Smart Homes
UR - http://www.scopus.com/inward/record.url?scp=85063482471&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85063482471&partnerID=8YFLogxK
U2 - 10.1109/ISC2.2018.8656942
DO - 10.1109/ISC2.2018.8656942
M3 - Conference contribution
AN - SCOPUS:85063482471
T3 - 2018 IEEE International Smart Cities Conference, ISC2 2018
BT - 2018 IEEE International Smart Cities Conference, ISC2 2018
PB - Institute of Electrical and Electronics Engineers Inc.
ER -