<manifestation xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:tucdl="http://purl.tuc.gr/dl/dias/schemas/aip/tucdl/" xmlns="http://purl.tuc.gr/dl/dias/schemas/aip/tucdl/" keyIdentifier="http://purl.tuc.gr/dl/dias/8E462CB7-F0CC-429C-A18F-51F26AD0D7D7" xsi:schemaLocation="http://purl.tuc.gr/dl/dias/schemas/aip/tucdl/ http://purl.tuc.gr/dl/dias/schemas/aip/tucdl"><titleOfTheManifestation>Kampelis_et_al_Energies_11(11)_2018.pdf</titleOfTheManifestation><isEmbodimentOf entityType="Expression"><uri>http://purl.tuc.gr/dl/dias/D6D5C92C-F485-480A-8C2F-0E00CA5BABAA</uri><title xml:lang="en">Development of demand response energy management optimization at building and district levels using genetic algorithm and artificial neural network modelling power predictions</title></isEmbodimentOf><accessRestrictionOnTheManifestation>free</accessRestrictionOnTheManifestation><dateOfPublicationDistribution>2019-05-27</dateOfPublicationDistribution><formOfCarrier>application/pdf</formOfCarrier><extentOfTheCarrier xml:lang="en">6.4 MB</extentOfTheCarrier></manifestation>