Demand response offers the possibility of altering the profile of power consumption of individual buildings or building districts, i.e., microgrids, for economic return. There is significant potential of demand response in enabling flexibility via advanced grid management options, allowing higher renewable energy penetration and efficient exploitation of resources. Demand response and distributed energy resource dynamic management are gradually gaining importance as valuable assets for managing peak loads, grid balance, renewable energy source intermittency, and energy losses. In this paper, the potential for operational optimization of a heating, ventilation, and air conditioning (HVAC) system in a smart near-zero-energy industrial building is investigated with the aid of a genetic algorithm. The analysis involves a validated building energy model, a model of energy cost, and an optimization model for establishing HVAC optimum temperature set points. Optimization aims at establishing the trade-off between the minimum daily cost of energy and thermal comfort. Predicted mean vote is integrated in the objective function to ensure thermal comfort requirements are met.
Nowadays, the design and use of multi-functional mortars has increased significantly, with interesting applications in the green building and cultural heritage conservation sectors. A key point for a correct adoption of these innovative materials is their behavior along time and their resistance to the weathering. The objective of this project was to define the performance and durability of innovative mortars, in order to use them correctly and to avoid irreparable damage over time. For the development of this project, lime–metakaolin and hydraulic lime–metakaolin based mortars (hereinafter called A, B), as well as A and B with the addition of nano-TiO2 and perlite (hereinafter referred to as A+, B+), have been tested. The focus of the work was to carry out preliminary tests to evaluate the performance and durability characteristics of these mortars, verifying their behavior over time through exposure to artificial aging cycles, including thermal shock cycles in saline solution aerosols, freeze cycles in vapor aerosol, and aging by heat treatment at high temperatures. Before and after each artificial aging cycle, weight measurements, and macroscopic and microscopic observations were performed in order to evaluate possible structural changes. The characteristics of the mortars were assessed by determination of the apparent volume mass, mechanical properties, such as compressive and bending strength, water absorption, whereas their self-cleaning capacity was measured by methylene blue degradation test under UV and solar irradiation. The results obtained show degradation effects in the mortar samples due to aging after each test, and indicated that mortars with perlite and nano-TiO2 are the best-performing ones, both from the durability and energetic point of view, rendering them suitable for applications in the green building sector and the conservation of cultural heritage.
Ambitious goals for the upgrade of construction aimed at energy conservation may quite often bounce due to different barriers, from design and detailing, through construction, to commissioning and behavioural ones. To overcome these, this paper focuses on the missing link of the construction process – the Informative Commissioning, the pre and post occupancy monitoring, and the POE, all integral parts of the ZeroPlus EU project. Limitations, barriers and other considerations and complexities encountered through the project’s stages will be presented, alongside the formation of the methodology and some preliminary results of the first case studies to be commissioned.
Demand response offers the possibility of altering the profile of power consumption of individual buildings or building districts, i.e., microgrids, for economic return. There is significant potential of demand response in enabling flexibility via advanced grid management options, allowing higher renewable energy penetration and efficient exploitation of resources. Demand response and distributed energy resource dynamic management are gradually gaining importance as valuable assets for managing peak loads, grid balance, renewable energy source intermittency, and energy losses. In this paper, the potential for operational optimization of a heating, ventilation, and air conditioning (HVAC) system in a smart near-zero-energy industrial building is investigated with the aid of a genetic algorithm. The analysis involves a validated building energy model, a model of energy cost, and an optimization model for establishing HVAC optimum temperature set points. Optimization aims at establishing the trade-off between the minimum daily cost of energy and thermal comfort. Predicted mean vote is integrated in the objective function to ensure thermal comfort requirements are met.
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