SG11201810279QA - Large scale machine learning-based chiller plants modeling, optimization and diagnosis - Google Patents

Abstract

INTERNATIONAL APPLICATION PUBLISHED UNDER THE PATENT COOPERATION TREATY (PCT) (19) World Intellectual Property -., Organization 03 11111111111110111011111111111011111010111HOIH01111011111100111111111011110111111 International Bureau (10) International Publication Number (43) International Publication Date .....•\"\" WO 2018/004464 Al 04 January 2018 (04.01.2018) W I PO I PCT (51) International Patent Classification: DZ, EC, EE, EG, ES, FI, GB, GD, GE, GH, GM, GT, HN, G05B 13/04 (2006.01) F24F 11/00 (2006.01) HR, HU, ID, IL, IN, IR, IS, JO, JP, KE, KG, KH, KN, KP, (21) International Application Number: KR, KW, KZ, LA, LC, LK, LR, LS, LU, LY, MA, MD, ME, PCT/SG2017/050324 MG, MK, MN, MW, MX, MY, MZ, NA, NG, NI, NO, NZ, OM, PA, PE, PG, PH, PL, PT, QA, RO, RS, RU, RW, SA, (22) International Filing Date: SC, SD, SE, SG, SK, SL, SM, ST, SV, SY, TH, TJ, TM, TN, 29 June 2017 (29.06.2017) TR, TT, TZ, UA, UG, US, UZ, VC, VN, ZA, ZM, ZW. (25) Filing Language: English (84) Designated States (unless otherwise indicated, for every (26) Publication Language: English kind of regional protection available): ARIPO (BW, GH, GM, KE, LR, LS, MW, MZ, NA, RW, SD, SL, ST, SZ, TZ, (30) Priority Data: UG, ZM, ZW), Eurasian (AM, AZ, BY, KG, KZ, RU, TJ, 10201605346S 29 June 2016 (29.06.2016) SG TM), European (AL, AT, BE, BG, CH, CY, CZ, DE, DK, EE, ES, FL FR, GB, GR, HR, HU, IE, IS, IT, LT, LU, LV, (71) Applicant: KIRKHAM GROUP PTE LTD [SG/SG]; MC, MK, MT, NL, NO, PL, PT, RO, RS, SE, SI, SK, SM, 1 Fusionopolis Walk, #02-11 North Tower, Singapore TR), OAPI (BF, BJ, CF, CG, CI, CM, GA, GN, GQ, GW, 138628 (SG). KM, ML, MR, NE, SN, TD, TG). (72) Inventors: CHAI, Kok Soon; 385, Goodview Gardens, Bukit Batok West Avenue 5, #11-334, Singapore 650385 Declarations under Rule 4.17: (SG). LAI, Choon Hoo; 25, Hazel Park Terrace, #18-02, — of inventorship (Rule 4.17(iv)) — Singapore 678948 (SG). Published: (74) Agent: AMICA LAW LLC; 30 Raffles Place, #14-01 — with international search report (Art. 21(3)) = Chevron House, Singapore 048622 (SG). = _ (81) Designated States (unless otherwise indicated, for every — kind of national protection available): AE, AG, AL, AM, — AO, AT, AU, AZ, BA, BB, BG, BH, BN, BR, BW, BY, BZ, = = CA, CH, CL, CN, CO, CR, CU, CZ, DE, DJ, DK, DM, DO, = (54) Title: LARGE SCALE MACHINE LEARNING-BASED CHILLER PLANTS MODELING, OPTIMIZATION AND DIAG- = NOSIS = (57) : The invention relates to a data driven, or a hybrid rule-based = = Determine the period of data for analysis based on 2 different objectives— 1) objective 1 —continuous ..'\",\"' chiller plant representation learning and optimization, objective 2 —Story telling from the M&V data 401 and data driven Energy/Building Management System, such as for chiller plants, which has ability to learn from the data and evaluate performance. According to the invention, a computer-implemented method trains prediction models for - = Decompose data Into different periods — baseline, cross validation and test baseline data, 03 each equipment model and chiller plant model using predicts a j- for each equipment model and chiller model using baseline da- parameter plant 405 ta, computes differential parameter of each equipment based on the predicted = = = = Train equipment models by neural network model for prediction, Gaussian process etc. 407a 407b 40 407d and actual parameters of each equipment, computes differential parameter of the chiller plant based on the predicted and actual parameters of the chiller plant, compute a differential parameter resulting from chiller plant optimization, by subtracting the differential parameters of the various equipment from the differ- CT model to predict CT power CHWP model to predict CHWP power CWP model to predict CWP power Chiller model M predict Chiller power ential parameter of the chiller plant,ascertaining a presence of abnormality in the differential resulting from chiller plant optimization and generating a parameter 409a 409b 409c • „,./ ...\"/ notification if the differential parameter resulting from chiller plant optimization is ascertained abnormal. 11 7r Predict CT, CHWP, CWP and Chiller power (baseline) Predict CT, CHWP. CWP and Chiller power (cross validation) Predict CT, CHWP. CWP and Chiller power (test405) • 411a • 411b ,./ ..-,/ 411c 71' 7r © © Regression on deviations, accuracies values. Regression on deviations, accuracies values. Regression on deviations, accuracies values. ..„.... GC 413 415 417 rr ,.../ v Evalua effectiveness o 11 C::) N Compute the fitness of — baseline data for ct, chwp. own and chillers / Ascertain model Analyzing predicted and actual power to identify any change in equipment performance of chine plant optimization tOntirnIze control set points C accuracy Figure 4