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

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. IndexTerms-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).

A Day-Ahead Optimal Economic Dispatch Schedule for Building CCHP System Based on Centralized Energy Storage Infrastructure

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Multienergy Load Forecasting for Regional Integrated Energy Systems Considering Multienergy Coupling of Variation Characteristic Curves

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