: 1-10
Received: October 10, 2019
Accepted: November 20, 2019
Lviv Politechniс National University, Department of Automated Control Systems
Lviv Politechnik National University, Department of Automated Control Systems
Lviv Polytechnic National University, Department of Automated Control Systems

It is shown that energy ef­fi­ci­ency impro­ve­ment of the re­gi­on's eco­nomy is re­ali­zed thro­ugh the use of in­for­ma­ti­on-analyti­cal me­ans of sup­por­ting energy ef­fi­ci­ency ma­na­ge­ment, which are ba­sed on in­tel­lec­tu­al in­for­ma­ti­on, Web and te­le­com­mu­ni­ca­ti­on techno­lo­gi­es. Archi­tec­tu­re of an in­for­ma­ti­on-analyti­cal system (IAS) for ma­na­ging the energy ef­fi­ci­ency of the re­gi­on's eco­nomy has be­en de­ve­lo­ped ba­sed on the prin­cip­les of mo­du­la­rity, open­ness, com­pa­ti­bi­lity and use of a set of ba­sic de­sign so­lu­ti­ons. IAS pro­vi­des col­lec­ti­on, pro­ces­sing and vis­ua­li­za­ti­on of energy da­ta, mo­de­ling, fo­re­cas­ting of energy ef­fi­ci­ency ma­na­ge­ment pro­ces­ses and sup­port of energy ef­fi­ci­ency ma­na­ge­ment de­ci­si­ons for re­gi­onal eco­no­mic. The cre­ati­on of a uni­fi­ed in­for­ma­ti­on spa­ce with re­li­ab­le, comple­te and ti­mely in­for­ma­ti­on that is used to ge­ne­ra­te ef­fec­ti­ve ma­na­ge­ment de­ci­si­ons is en­su­red. On the ba­sis of the In­ter­net of Things con­cept de­ve­lo­ped da­ta col­lec­tors that are the spa­ti­ally distri­bu­ted small in­tel­li­gent sen­sors lin­ked to a clo­ud ser­ver. It is shown that it is ex­pe­di­ent to de­ve­lop the com­po­nents of the geoin­for­ma­ti­on system for the IACEA re­gi­on eco­nomy using Go­og­le Clo­ud Ser­vi­ces and the spe­ci­ali­zed Go­og­le Maps API, which will pro­vi­de promptly cre­ati­on, mo­di­fi­ca­ti­on and incre­ase of in­for­ma­ti­on ca­pa­bi­li­ti­es. It is ar­gu­ed that the ad­di­ti­onal in­vol­ve­ment of prog­ram­ming to­ols, inclu­ding Ja­vaScript, using the Go­og­le Maps API pro­vi­des the op­por­tu­nity to de­ve­lop a geoin­for­ma­ti­on system for the IAS for sup­por­ting energy ef­fi­ci­ency ma­na­ge­ment of re­gi­onal eco­nomy, ta­king in­to ac­co­unt ad­di­ti­onal spe­ci­fic fu­tu­re req­ui­re­ments of the thesis system. It is pro­po­sed cre­ati­on of the IAS for sup­por­ting energy ef­fi­ci­ency ma­na­ge­ment on the ba­sis of da­ta­ba­ses and da­ta wa­re­hou­ses, spe­ci­ali­zed pub­licly ava­ilab­le GIS to­ols for vis­ua­li­za­ti­on and analysis of energy con­sumpti­on and energy ef­fi­ci­ency da­ta, which will en­su­re the fe­asi­bi­lity and ef­fi­ci­ency of ge­ne­ra­ted ma­na­ge­ment de­ci­si­ons. It is shown that the vis­ua­li­za­ti­on of energy da­ta and pro­ces­sing re­sults in the most hu­man-re­adab­le form with pre­ci­se lo­ca­ti­ons of the ma­na­ge­ment fa­ci­li­ti­es pro­vi­des ef­fec­ti­ve sup­port for ma­na­ge­ment de­ci­si­ons.

[1]     Ber­toc­co, M., Cap­pel­laz­zo, S., Flam­mi­ni, A., & Par­vis, M. (2002). A mul­ti-la­yer archi­tec­tu­re for distri­bu­ted da­ta ac­qui­si­ti­on. Pro­ce­edings of the 19th IEEE Instru­men­ta­ti­on and Me­asu­re­ment Techno­logy Con­fe­ren­ce, 2, 1261–1264.

[2]     Big Da­ta Imple­men­ta­ti­on Vs. (2019). Da­ta Wa­re­hou­sing. Ret­ri­eved from:

[3]     Bulls, N. (2018). Big Da­ta Vis­ua­li­za­ti­on To­ols. Encyclo­pe­dia of Big Da­ta Techno­lo­gi­es. Sprin­ger. Ret­ri­eved from:

[4]     Chiu, Yu. Hsi­en, et al. (2014). En­terpri­se re­so­ur­ce plan­ning. New-York, 268 p.

[5]     Da­ta Vis­ua­li­za­ti­on. (2015). Da­ta Vis­ua­li­za­ti­on for Hu­man Per­cep­ti­on. The In­te­rac­ti­on De­sign Fo­un­da­ti­on. Ret­ri­eved from:

[6]     Da­ta Wa­re­hou­se De­sign. (2019). Ret­ri­eved from:

[7]     Da­ta Wa­re­hou­sing. (2019). Big Da­ta and Its Im­pact on Da­ta Wa­re­hou­sing. Ret­ri­eved from:

[8]     Di­aman­ta­ras, K. I., & Kung, S. Y. (1996). Prin­ci­pal Com­po­nent Neu­ral Net­works: The­ory and Appli­ca­ti­ons. Wi­ley, 270 p.

[9]     Fri­ed­man, V. (2008). Da­ta Vis­ua­li­za­ti­on and In­fog­rap­hics. in: Grap­hics, Mon­day Inspi­ra­ti­on, Jan­uary 14th, 2008. Ret­ri­eved from:

[10]  Kar­pa, D. M., Tsmots, I. G., & Teslyuk, V. M. (2019). De­ci­si­on sup­port to­ols for pri­ori­ti­zing energy-sa­ving pro­jects. Sci­en­ti­fic Bul­le­tin of UN­FU, 29(2), 135–140. [In Uk­ra­ini­an].

[11]  Lambda Archi­tec­tu­re. (2019). Ret­ri­eved from:

[12]  Lengler, Ralph, & Eppler, Mar­tin. J. (2019). Pe­ri­odic Tab­le of Vis­ua­li­za­ti­on Met­hods. Ret­ri­eved from:

[13]  Me­di­kovsky, M. O., Tsmots, I. G., & Po­dolsky, M. R. (2013). Substan­ti­ati­on of the prin­cip­les of construc­ti­on and de­ve­lop­ment of the ge­ne­ra­li­zed struc­tu­re of the in­for­ma­ti­on-analyti­cal system for es­ti­ma­ti­on, fo­re­cas­ting and ma­na­ge­ment of energy ef­fi­ci­ency of the re­gi­on's eco­nomy. (Ser. Com­pu­ter sci­en­ces and in­for­ma­ti­on techno­lo­gi­es). Bul­le­tin of NU "Lviv Polytechnic", 751, 40–51. Lviv. [In Uk­ra­ini­an].

[14]  Medykovsky, M. O., Tkac­hen­ko, R. O., Tsmots, I. G., Tsym­bal, Yu. V., Do­ros­hen­ko, A. V., & Sko­rok­ho­da, O. V. (2015). In­tel­lec­tu­al com­po­nents of in­teg­ra­ted au­to­ma­ted control systems. Lviv: Lviv Polytechnic Pub­lis­hing Hou­se, 280 p. [In Uk­ra­ini­an].

[15]  Medykovskyi, M. O., Tsmots, I. G., & Sko­rok­ho­da, О. В. (2014). Spectrum neu­ral net­work filtra­ti­on techno­logy for impro­ving the fo­re­cast ac­cu­racy of dyna­mic pro­ces­ses in eco­no­mics. Ac­tu­al Prob­lems of Eco­no­mics, 12(162), 410–416.

[16]  Medykovskyi, M. O., Tsmots, I. G., & Tsymbal, Yu. V. (2013). In­tel­li­gent da­ta pro­ces­sing to­ols in energy ef­fi­ci­ency ma­na­ge­ment systems for re­gi­onal eco­nomy. Ac­tu­al Prob­lems of Eco­no­mics, 12(150), 271–277.

[17]  Medykovskyi, M. O., Tsmots, I. G., Sko­rok­ho­da, О. В., & Teslyuk, T. V. (2016). De­sign of In­tel­li­gent Com­po­nent of Hi­erarchi­cal Control System. Econ­techmod: An In­ter­na­ti­onal Qu­ar­terly Jo­ur­nal, 5(2.3), 3–10.

[18]  Medykovskyi, M. O., Tsmots, I. H., & Tsymbal, Yu. V. (2016). In­for­ma­ti­on analyti­cal system for energy ef­fi­ci­ency ma­na­ge­ment at en­terpri­ses in the city of Lviv (Uk­ra­ine). Ac­tu­al Prob­lems of Eco­no­mics, 1(175), 379–384.

[19]  O'Le­ary, D. E. (2000). En­terpri­se re­so­ur­ce plan­ning systems: systems, li­fe cycle, electro­nic com­mer­ce, and risk. Cambrid­ge Uni­ver­sity Press.

[20]  Teslyuk, T., Tsmots, I., Teslyuk, V., Medykovskyi, M., & Opot­yak, Y. (2017). Archi­tec­tu­re of the ma­na­ge­ment system of energy ef­fi­ci­ency of techno­lo­gi­cal pro­ces­ses in the en­terpri­se. Pro­ce­edings of the 12th In­ter­na­ti­onal Sci­en­ti­fic and Techni­cal Con­fe­ren­ce, Sep­tem­ber 5–8, 2017, Lviv, (pp. 429–433).

[21]  Teslyuk, T., Tsmots, I., Teslyuk, V., Medykovskyy, M., & Opot­yak, Y. (2018). Archi­tec­tu­re and Mo­dels for System-Le­vel Com­pu­ter-Aided De­sign of the Energy Ef­fi­ci­ency Ma­na­ge­ment System of Techno­lo­gi­cal Pro­ces­ses at the En­terpri­se. Au­to­ma­ti­on 2017. Ad­van­ces in In­tel­li­gent Systems and Com­pu­ting: In­ter­na­ti­onal Con­fe­ren­ce, 689, 538–557. Sprin­ger.

[22]  Tsmot, I. G., Teslyuk, T. V., Opot­yak, Yu. V., & Teslyuk, V. M. (2017). Archi­tec­tu­re of a Mul­ti­le­vel Energy Ef­fi­ci­ency Ma­na­ge­ment System in the Re­gi­on. (Ser. Com­pu­ter Sci­en­ce and In­for­ma­ti­on Techno­lo­gi­es). Bul­le­tin of the Na­ti­onal Techni­cal Uni­ver­sity of Lviv Polytechnic, 864, 201–209. Lviv. [In Uk­ra­ini­an].

[23]  Tsmots, I. G., & Ro­man, V. I. (2019). Impro­ving the met­hod of grou­ping energy da­ta in the system of mul­ti­le­vel energy ef­fi­ci­ency ma­na­ge­ment of the re­gi­on's eco­nomy. Sci­en­ti­fic Bul­le­tin of UN­FU, 29(1), 116–120. [In Uk­ra­ini­an].

[24]  Tsmots, I. G., Sko­rok­ho­da, O. V., & Ro­man, V. I. (2016). Da­ta re­po­si­to­ri­es of mul­ti­le­vel energy ef­fi­ci­ency ma­na­ge­ment systems. Mo­de­ling and in­for­ma­ti­on techno­lo­gi­es, 77, 192–197. Insti­tu­te of Mo­de­ling Prob­lems in Energy. [In Uk­ra­ini­an].

[25]  Tsmots, I. G., Tsymbal, Yu. V., & Tsmots, O. I. (2012). Early war­ning systems for en­terpri­ses using neu­ral net­works to­ols. Ac­tu­al Prob­lems of Eco­no­mics, 10(136), 283–291.

[26]  Tsymbal, Yu., & Tkac­hen­ko, R. (2016). A Di­gi­tal Wa­ter­mar­king Sche­me Ba­sed on Au­to­as­so­ci­ati­ve Neu­ral Net­works of the Ge­omet­ric Transfor­ma­ti­ons Mo­del. Pro­ce­edings of the 2016 IEEE First In­ter­na­ti­onal Con­fe­ren­ce on Da­ta Stre­am Mi­ning & Pro­ces­sing, (pp. 231–234).