Next-day daily energy consumption forecast model development and model implementation / Li Song in Transactions of the ASME. Journal of solar energy engineering, Vol. 134 N° 3 (Août 2012)  

Public ISBD [article]  inTransactions of the ASME. Journal of solar energy engineering > Vol. 134 N° 3 (Août 2012) . - 08 p. Titre : Next-day daily energy consumption forecast model development and model implementation Type de document : texte imprimé Auteurs : Li Song, Auteur ; Ik-seong Joo, Auteur ; Subroto Gunawan, Auteur Année de publication : 2012 Article en page(s) : 08 p. Note générale : solar energy Langues : Anglais (eng) Mots-clés : thermal storage systems  cooling consumption  autoregressive model  linear regression control algorithm Index. décimale : 621.47 Résumé : Thermal storage systems were originally designed to shift on-peak cooling production to off-peak cooling production in order to reduce on-peak electricity demand. Recently, however, the reduction of both on- and off-peak demand is a critical issue. Reduction of on- and off-peak demand can also extend the life span and defer or eliminate the replacement of power transformers. Next day electricity consumption is a critical set point to operate chillers and associated pumps at the appropriate time. In this paper, a data evaluation process using the annual daily average cooling consumption of a building was conducted. Three real-time building load forecasting models were investigated: a first-order autoregressive model (AR(1)), an autogressive integrated moving average model (ARIMA(0,1,0)), and a linear regression model. A comparison of results shows that the AR(1) and ARIMA(0,1,0) models provide superior results to the linear regression model, except that the AR(1) model has a few unacceptable spikes. A complete control algorithm integrated with a corrected AR(1) forecast model for a chiller plant including chillers, thermal storage system, and pumping systems was developed and implemented to verify the feasibility of applying this algorithm in the building automation system. Application results are also introduced in the paper. DEWEY : 621.47 ISSN : 0199-6231 En ligne : http://asmedl.org/getabs/servlet/GetabsServlet?prog=normal&id=JSEEDO000134000003 [...] [article] Next-day daily energy consumption forecast model development and model implementation [texte imprimé] / Li Song, Auteur ; Ik-seong Joo, Auteur ; Subroto Gunawan, Auteur . - 2012 . - 08 p. solar energy Langues : Anglais (eng) in Transactions of the ASME. Journal of solar energy engineering > Vol. 134 N° 3 (Août 2012) . - 08 p. Mots-clés : thermal storage systems  cooling consumption  autoregressive model  linear regression control algorithm Index. décimale : 621.47 Résumé : Thermal storage systems were originally designed to shift on-peak cooling production to off-peak cooling production in order to reduce on-peak electricity demand. Recently, however, the reduction of both on- and off-peak demand is a critical issue. Reduction of on- and off-peak demand can also extend the life span and defer or eliminate the replacement of power transformers. Next day electricity consumption is a critical set point to operate chillers and associated pumps at the appropriate time. In this paper, a data evaluation process using the annual daily average cooling consumption of a building was conducted. Three real-time building load forecasting models were investigated: a first-order autoregressive model (AR(1)), an autogressive integrated moving average model (ARIMA(0,1,0)), and a linear regression model. A comparison of results shows that the AR(1) and ARIMA(0,1,0) models provide superior results to the linear regression model, except that the AR(1) model has a few unacceptable spikes. A complete control algorithm integrated with a corrected AR(1) forecast model for a chiller plant including chillers, thermal storage system, and pumping systems was developed and implemented to verify the feasibility of applying this algorithm in the building automation system. Application results are also introduced in the paper. DEWEY : 621.47 ISSN : 0199-6231 En ligne : http://asmedl.org/getabs/servlet/GetabsServlet?prog=normal&id=JSEEDO000134000003 [...]

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