Abstract
The objective of the present work is to develop a neural network computer program to predict the next-day cooling load and use this prediction in conjunction with a real-time expert system to simulate management of a cold thermal storage system. The next-day cooling load prediction allows the ice thermal storage system to maximize off-peak utility rates and minimize mechanical system operation. The management system is designed to be used by mechanical engineers in the field of air-conditioning control and maintenance. The computer program is written with the aid of commercially available neural network computer software. The temperature data base includes hourly ambient temperature values for a typical year in Miami, Florida. The technique used to predict the required ice production of the thermal storage system is conducted by training a neural network with the use of definition, fact, and training network files. Once the network is trained, any temperature pattern for a 24-hour period can be used to calculate the required next-day cooling load. Neural network training included unique temperature patterns for 'cold' and 'warm' weather fronts, as well as seasonally adjusted 'normal' temperature patterns. The resulting cold thermal storage prediction is then entered into a real-time expert system for the control of the overnight chiller operation and ice storage production.
| Original language | English (US) |
|---|---|
| Title of host publication | ASHRAE Transactions |
| Editors | Anon |
| Publisher | Publ by ASHRAE |
| Pages | 723-726 |
| Number of pages | 4 |
| Edition | pt 2 |
| State | Published - 1990 |
| Event | 1990 Annual Meeting of the American Society of Heating, Refrigerating and Air-Conditioning Engineers, Technical and Symposium Papers - St. Louis, MO, USA Duration: Jun 10 1990 → Jun 13 1990 |
Other
| Other | 1990 Annual Meeting of the American Society of Heating, Refrigerating and Air-Conditioning Engineers, Technical and Symposium Papers |
|---|---|
| City | St. Louis, MO, USA |
| Period | 6/10/90 → 6/13/90 |
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ASJC Scopus subject areas
- Fluid Flow and Transfer Processes
Cite this
Prediction of thermal storage loads using a neural network. / Ferrano, Frank J.; Wong, Kaufui.
ASHRAE Transactions. ed. / Anon. pt 2. ed. Publ by ASHRAE, 1990. p. 723-726.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Prediction of thermal storage loads using a neural network
AU - Ferrano, Frank J.
AU - Wong, Kaufui
PY - 1990
Y1 - 1990
N2 - The objective of the present work is to develop a neural network computer program to predict the next-day cooling load and use this prediction in conjunction with a real-time expert system to simulate management of a cold thermal storage system. The next-day cooling load prediction allows the ice thermal storage system to maximize off-peak utility rates and minimize mechanical system operation. The management system is designed to be used by mechanical engineers in the field of air-conditioning control and maintenance. The computer program is written with the aid of commercially available neural network computer software. The temperature data base includes hourly ambient temperature values for a typical year in Miami, Florida. The technique used to predict the required ice production of the thermal storage system is conducted by training a neural network with the use of definition, fact, and training network files. Once the network is trained, any temperature pattern for a 24-hour period can be used to calculate the required next-day cooling load. Neural network training included unique temperature patterns for 'cold' and 'warm' weather fronts, as well as seasonally adjusted 'normal' temperature patterns. The resulting cold thermal storage prediction is then entered into a real-time expert system for the control of the overnight chiller operation and ice storage production.
AB - The objective of the present work is to develop a neural network computer program to predict the next-day cooling load and use this prediction in conjunction with a real-time expert system to simulate management of a cold thermal storage system. The next-day cooling load prediction allows the ice thermal storage system to maximize off-peak utility rates and minimize mechanical system operation. The management system is designed to be used by mechanical engineers in the field of air-conditioning control and maintenance. The computer program is written with the aid of commercially available neural network computer software. The temperature data base includes hourly ambient temperature values for a typical year in Miami, Florida. The technique used to predict the required ice production of the thermal storage system is conducted by training a neural network with the use of definition, fact, and training network files. Once the network is trained, any temperature pattern for a 24-hour period can be used to calculate the required next-day cooling load. Neural network training included unique temperature patterns for 'cold' and 'warm' weather fronts, as well as seasonally adjusted 'normal' temperature patterns. The resulting cold thermal storage prediction is then entered into a real-time expert system for the control of the overnight chiller operation and ice storage production.
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M3 - Conference contribution
AN - SCOPUS:0025547320
SP - 723
EP - 726
BT - ASHRAE Transactions
A2 - Anon, null
PB - Publ by ASHRAE
ER -