NDLI: Load Forecasting and Operation Strategy Design for CCHP Systems Using Forecasted Loads

Content Provider	IEEE Xplore Digital Library
Author	Mingxi Liu Yang Shi Fang Fang
Copyright Year	1993
Abstract	Operation strategy of combined cooling, heating, and power (CCHP) systems is designed to collect users' load information to determine the energy input to the system and power flow inside the system. Most of the current operation strategies are designed by assuming that accurate loads during the next time interval are already known. To solve the problem of unknown loads in practical applications, using an autoregressive moving average with exogenous inputs model, whose parameters are identified by an ordinary least squares-two-stage recursive least squares (TSRLS) algorithm, cooling, heating, and electrical loads in the future time intervals are forecasted. The identification procedure uses the dew-point temperature as the instrumental variable for the exogenous variable (dry-bulb temperature), to better characterize the relationship between exogenous and endogenous variables. TSRLS helps to reduce the space and time complexity. A poststrategy is also proposed to compensate for the inaccurate forecasting. A case study is conducted to verify the feasibility and effectiveness of the proposed methods.
Sponsorship	IEEE Control Systems Society
Starting Page	1672
Ending Page	1684
Page Count	13
File Size	4094815
File Format	PDF
ISSN	10636536
Volume Number	23
Issue Number	5
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Load modeling Predictive models Forecasting Autoregressive processes Cooling Resistance heating Estimation two-stage recursive least squares (TSRLS). Autoregressive moving average with exogenous (ARMAX) input combined cooling heating and power (CCHP) load forecasting optimal operation strategy poststrategy two-stage recursive least squares (TSRLS) power (CCHP)
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Electrical and Electronic Engineering

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

A particle swarm optimization to identifying the ARMAX model for short-term load forecasting

A Two-Stage Industrial Load Forecasting Scheme for Day-Ahead Combined Cooling, Heating and Power Scheduling

Load forecasting via suboptimal seasonal autoregressive models and iteratively reweighted least squares estimation

A New Two-Stage Approach to Short Term Electrical Load Forecasting

Operation optimization of CCHP-type microgrid considering units' part-load characteristics

Load Forecast Model Switching Scheme for Improved Robustnessto Changes in Building Energy Consumption Patterns

Regularised estimation for ARMAX process with measurements subject to outliers

Bias compensation-based recursive least-squares estimation with forgetting factors for output error moving average systems

An Improved Combined Forecasting Method for Electric Power Load Based on Autoregressive Integrated Moving Average Model

Load Forecasting and Operation Strategy Design for CCHP Systems Using Forecasted Loads

Similar Documents

A particle swarm optimization to identifying the ARMAX model for short-term load forecasting

A Two-Stage Industrial Load Forecasting Scheme for Day-Ahead Combined Cooling, Heating and Power Scheduling

Load forecasting via suboptimal seasonal autoregressive models and iteratively reweighted least squares estimation

A New Two-Stage Approach to Short Term Electrical Load Forecasting

Operation optimization of CCHP-type microgrid considering units' part-load characteristics

Load Forecast Model Switching Scheme for Improved Robustnessto Changes in Building Energy Consumption Patterns

Regularised estimation for ARMAX process with measurements subject to outliers

Bias compensation-based recursive least-squares estimation with forgetting factors for output error moving average systems

An Improved Combined Forecasting Method for Electric Power Load Based on Autoregressive Integrated Moving Average Model

Load Forecasting and Operation Strategy Design for CCHP Systems Using Forecasted Loads