Annual building renovation rates in Europe currently stand at 0.4 to 1.2%. In order for Europe to meetits energy efficiency targets a “renovation wave” will have to be triggered that will, at least double, the current rates (“A European Green Deal | European Commission” 2019). It is clear, in the “Clean Energy Package for All Europeans”, that the European Commission regards Energy Performance Contracting (EPC) as a key tool to boost the aforementioned “renovation wave”. This is a renovation model in which the client shares the performance and financial risk of the energy efficiency renovation with the Energy Service (ESCO), responsible for designing, implementing, and operating the project during its lifetime. This is a model that has not seen the expected uptake in Europe its potential suggested, due to a set of, already well identified, regulatory, market, financial and social barriers. This project proposes an innovative EPC model – the Integrated Benefits Model – that aims at tackling some of the current barriers and envisions what the future of energy consumption in buildings can be. This model was tested in a real case study and was shown to reduce the project’s payback time by 16% when compared to a traditional energy efficiency renovation. This increases the attractiveness of energy retrofits among building owners. To address some of the remaining barriers, a set of recommendations to stakeholders was drafted, in order to facilitate a wider adoption of EPCs (and in particular the Integrated Benefits Model) across the whole value chain. / Byggnadsrenoveringsgraden ligger för närvarande på 0,4 till 1,2%. För att Europa ska kunna uppnå sina energieffektivitetsmål måste en ”renoveringsvåg” utlösas som åtminstone kommer att fördubbla den uvarande siffrorona (“A European Green Deal | European Commission” 2019). Det är tydligt i satsningen "Ren energi för alla européer" att Europeiska kommissionen ser Energy Performance Contracting (EPC) som ett nyckelverktyg för att utlösa den ovannämnda "renoveringsvågen". Detta är en renoveringsmodell där kunden delar prestanda och finansiell risk för energieffektivitetsrenoveringen med ett s.k. Energy Service Company (ESCO), som ansvarar för att utforma, implementera och driva projektet under dess livstid. Detta är dock en modell som inte har utvecklats som väntat i Europa trots sin potential. Skälet till detta är på grund av en uppsättning väl identifierade reglerande, marknadsmässiga, finansiella och sociala hinder. Detta projekt föreslår en innovativ EPC-modell - Integrated Benefits Model - som syftar till att ta itu med några av de nuvarande hindren. Denna modell testades i en riktig fallstudie och visade sig minska projektets återbetalningstid med 16% jämfört med en traditionell energieffektivitetsrenovering. Detta ökar attraktiviteten för energieffektiviseringsåtgärder bland byggnadsägare. För att ta itu med några av de återstående hindren har en uppsättning rekommendationer utarbetades till intressenter för att möjliggöra EPC:er (och särskilt den integrerade förmånsmodellen) över hela värdekedjan.

Submitted by Nadir Basilio (nadirsb@uninove.br) on 2017-04-27T18:03:46Z No. of bitstreams: 1 Francisco Elanio Bezerra.pdf: 3173273 bytes, checksum: d46cdeb01bbd348e3926275e79daa4f3 (MD5) / Made available in DSpace on 2017-04-27T18:03:46Z (GMT). No. of bitstreams: 1 Francisco Elanio Bezerra.pdf: 3173273 bytes, checksum: d46cdeb01bbd348e3926275e79daa4f3 (MD5) Previous issue date: 2017-02-02 / The technological advance, in the world, has brought about profound changes in the way the electric energy is generated, distributed and consumed. The increase in electricity consumption and the interruption of power supply in Brazil led to the creation of Decree 5.163/2004, proposing a new model for the sale of electricity in the National Interconnected System through auctions in the free contracting environments between buyers and Sellers, or regulated through auctions promoted by the Electric Energy Trading Chamber (CCEE), which accounts for the difference between contracting and energy consumption and through the settlement price of the differences and promotes the settlement of this energy short-term market .If you have more contracts than consumption, or more consumption than contracts, you will suffer penalties. With the change in the commercialization of energy, the generators and distributors suffer with forecast of consumption and with amount of energy that must contract in the auctions. In this scenario, several techniques such as genetic algorithm, multicriteria decision, fuzzy logic, artificial neural networks among others have been used to optimize the system of buying and selling energy in this new environment. Therefore, the proposal of this work is to develop an intelligent computational system, using historical data from a distributor to forecast demand by consumption class to support decision making in the short term market. The result of the work may provide conditions for a distributor to monitor energy demand by consumption class, provide possibilities for short-term market trading and minimize losses with subcontracting and over-contracting. / O avanço tecnológico, no mundo, trouxe profundas mudanças na forma como a energia elétrica é gerada, distribuída e consumida. O aumento do consumo de energia elétrica e a interrupção no fornecimento de energia no Brasil levaram à criação do Decreto 5.163/2004, propondo um novo modelo de comercialização de energia elétrica no Sistema Interligado Nacional por meio de leilões nos ambientes de contratação livre entre compradores e vendedores, ou regulada, por meio de leilões promovidos pela Câmara de Comercialização de Energia Elétrica (CCEE). A diferença entre contratação e consumo é contabilizada pela CCEE mensalmente e negociada no mercado de curto prazo. Por meio do preço de liquidação das diferenças é promovida a liquidação dessa energia, cujo mecanismo pode gerar prejuízos ou lucros para a distribuidora que, caso tenha mais contratos do que consumo, ou mais consumo do que contratos, sofrerá penalizações. Com a modificação na comercialização de energia, os geradores e distribuidores sofrem com previsão de consumo e com montante de energia que devem contratar nos leilões. Neste cenário, diversas técnicas, como algoritmo genético, decisão multicritério, lógica fuzzy, redes neurais artificiais entre outras vêm sendo utilizadas para otimizar o sistema de compra e venda que atenda o decreto e mantenha as receitas da geradora e distribuidora. Sendo assim, a proposta deste trabalho é desenvolver uma abordagem computacional utilizando dados históricos de uma distribuidora para previsão de demanda por classe de consumo que sirva de apoio à tomada de decisão no mercado de curto prazo. O resultado do trabalho poderá oferecer condições para uma distribuidora acompanhar a demanda de energia por classe de consumo, fornecer possibilidades para negociação no mercado de curto prazo e minimizar prejuízos com subcontratação e sobrecontratação.