Purpose: Exterior shading devices and dynamic shading systems constitute an efficient way to improve energy efficiency and occupants’ comfort in buildings through the reduction of direct solar heat gains and disturbing glare. The purpose of this paper is to analyse the performance of different types of shading systems, fixed and dynamic, and their influence on the energy consumption and cooling loads for an office building located in Tallinn, Estonia. The scope is to determine the most performative configuration for energy consumption and cooling load reduction for office buildings and to provide designers and developers with the necessary knowledge to increase the performance of their buildings. Design/methodology/approach: There are many types of fixed shading devices, most of which use rectangular planar elements, the orientation and layout of which depends on the building location and façade orientation. The dynamic shading systems vary on the base of the building occupancy schedules and occupants’ preferences. The paper presents a method to determine the most efficient type and size of fixed shading devices in relation to different windows’ size and orientation, and the quantity of windows panes. At the same time the dynamic shading system using a control algorithm developed by the authors is compared. Findings: The results show that solar shading is an efficient way to control the energy consumption of office buildings, though with different efficacy by the static systems depending on orientation, window and shading type. Evidence shows that dynamic blind systems have more uniform performance and usually outperform static shading. Originality/value: The paper compares the performances of different static and dynamic shading devices and systems for the location in Tallinn. The dynamic shading system tested uses a control algorithm developed by the authors. The indications for the energy reduction and cooling loads are a valuable resource for designers and developers to increase the energy efficiency of their buildings.

District heating (DH) offers the most effective way to enhance the efficiency of primary energy use, increasing the share of renewable energy in energy consumption and decreasing the amount of CO₂ emissions. According to Article 9 section 1 of the Directive 2010/31/EU of the European Parliament and of the Council of 19 May 2010 on the energy performance of buildings, the Member states of the European Union are obligated to draw up National Plans for increasing the number of nearly zero-energy buildings [1]. Article 2 section 2 of the same Directive states that the energy used in nearly zero-energy buildings should be created covered to a very significant extent by energy from renewable sources, including energy from renewable sources produced on-site or nearby. Thus, the heat distributed by DH systems and produced by manufacturing devices located in close vicinity of the building also have to be taken into account in determining the energy consumption of the building and the share of renewable energy used in the nearly zero-energy buildings. With regard to the spreading of nearly zero-energy and zero-energy houses, the feasibility of on-site energy (heat and/or electricity) production and consumption in DH areas energy (i.e. parallel consumption, when the consumer, connected to DH system, consumes energy for heat production from other sources besides the DH system as well) needs to be examined. In order to do that, it is necessary to implement a versatile methodological approach based on the principles discussed in this article.

Large office buildings are responsible for a substantial portion of energy consumption in urban districts. However, thorough assessments regarding the Nordic countries are still lacking. In this paper we analyse the largest dataset to date for a Nordic office building, by considering a case study located in Stockholm, Sweden, that is occupied by nearly a thousand employees. Distinguishing the lighting and occupants’ appliances energy use from heating and cooling, we can estimate the impact of occupancy without any schedule data. A standard frequentist analysis is compared with Bayesian inference, and the according regression formulas are listed in tables that are easy to implement into building performance simulations (BPS). Monthly as well as seasonal correlations are addressed, showing the critical importance of occupancy. A simple method, grounded on the power drain measurements aimed at generating boundary conditions for the BPS, is also introduced; it shows how, for this type of data and number of occupants, no more complexities are needed in order to obtain reliable predictions. For an average year, we overestimate the measured cumulative consumption by only 4.7%. The model can be easily generalised to a variety of datasets.