Browsing by Keyword "info:eu-repo/grantAgreement/EC/H2020/680676/EU/Optimised Energy Efficient Design Platform for Refurbishment at District Level/OptEEmAL"

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    OptEEmAL: Decision-Support Tool for the Design of Energy Retrofitting Projects at District Level: Decision-Support Tool for the Design of Energy Retrofitting Projects at District Level
    (2019-06-21) García-Fuentes, M A; Hernández, G; Serna, V; Martín, S; Álvarez, S; Lilis, G N; Giannakis, G; Katsigarakis, K; Mabe, L; Oregi, X; Manjarres, D; Ridouane, H El; De Tommasi, L; PLANIFICACIÓN ENERGÉTICA; Tecnalia Research & Innovation; IA
    Designing energy retrofitting actions poses an elevated number of problems, as the definition of the baseline, selection of indicators to measure performance, modelling, setting objectives, etc. This is time-consuming and it can result in a number of inaccuracies, leading to inadequate decisions. While these problems are present at building level, they are multiplied at district level, where there are complex interactions to analyse, simulate and improve. OptEEmAL proposes a solution as a decision-support tool for the design of energy retrofitting projects at district level. Based on specific input data (IFC(s), CityGML, etc.), the platform will automatically simulate the baseline scenario and launch an optimisation process where a series of Energy Conservation Measures (ECMs) will be applied to this scenario. Its performance will be evaluated through a holistic set of indicators to obtain the best combination of ECMs that complies with user's objectives. A great reduction in time and higher accuracy in the models are experienced, since they are automatically created and checked. A subjective problem is transformed into a mathematical problem; it simplifies it and ensures a more robust decision-making. This paper will present a case where the platform has been tested.
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    Simulation-Based Evaluation and Optimization of Control Strategies in Buildings
    (2018-12-01) Kontes, Georgios; Giannakis, Georgios; Sánchez, Víctor; de Agustin-Camacho, Pablo; Romero-Amorrortu, Ander; Panagiotidou, Natalia; Rovas, Dimitrios; Steiger, Simone; Mutschler, Christopher; Gruen, Gunnar; Tecnalia Research & Innovation; EDIFICACIÓN DE ENERGÍA POSITIVA; LABORATORIO DE TRANSFORMACIÓN URBANA
    Over the last several years, a great amount of research work has been focused on the development of model predictive control techniques for the indoor climate control of buildings, but, despite the promising results, this technology is still not adopted by the industry. One of the main reasons for this is the increased cost associated with the development and calibration (or identification) of mathematical models of special structure used for predicting future states of the building. We propose a methodology to overcome this obstacle by replacing these hand-engineered mathematical models with a thermal simulation model of the building developed using detailed thermal simulation engines such as EnergyPlus. As designing better controllers requires interacting with the simulation model, a central part of our methodology is the control improvement (or optimisation) module, facilitating two simulation-based control improvement methodologies: one based in multi-criteria decision analysis methods and the other based on state-space identification of dynamical systems using Gaussian process models and reinforcement learning. We evaluate the proposed methodology in a set of simulation-based experiments using the thermal simulation model of a real building located in Portugal. Our results indicate that the proposed methodology could be a viable alternative to model predictive control-based supervisory control in buildings.
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    Two-Stage Multi-Objective Meta-Heuristics for Environmental and Cost-Optimal Energy Refurbishment at District Level
    (2019) Manjarres, Diana; Mabe, Lara; Oregi, Xabat; Landa-Torres, Itziar; Tecnalia Research & Innovation; IA; PLANIFICACIÓN ENERGÉTICA
    Energy efficiency and environmental performance optimization at the district level are following an upward trend mostly triggered by minimizing the Global Warming Potential (GWP) to 20% by 2020 and 40% by 2030 settled by the European Union (EU) compared with 1990 levels. This paper advances over the state of the art by proposing two novel multi-objective algorithms, named Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-Objective Harmony Search (MOHS), aimed at achieving cost-effective energy refurbishment scenarios and allowing at district level the decision-making procedure. This challenge is not trivial since the optimisation process must provide feasible solutions for a simultaneous environmental and economic assessment at district scale taking into consideration highly demanding real-based constraints regarding district and buildings’ specific requirements. Consequently, in this paper, a two-stage optimization methodology is proposed in order to reduce the energy demand and fossil fuel consumption with an affordable investment cost at building level and minimize the total payback time while minimizing the GWP at district level. Aimed at demonstrating the effectiveness of the proposed two-stage multi-objective approaches, this work presents simulation results at two real district case studies in Donostia-San Sebastian (Spain) for which up to a 30% of reduction of GWP at district level is obtained for a Payback Time (PT) of 2–3 years.