1. Introduction
The building sector in the European Union consumes around 40% of total final energy consumption, of which about 80% is spent on heating and domestic hot water preparation (the remainder consists of cooling with 0.6% and electric energy with 19.40%) [
1]. The first major incentive to increase energy efficiency and reduce energy consumption was made after the signing of the Kyoto Protocol [
2], which encouraged various approaches and methodologies for increasing energy efficiency in buildings, particularly in residential buildings [
3]. In contemporary research, a number of models have been developed to evaluate the effects of applying energy efficiency measures and forecasting consumption in the building sector, based on methods such as traditional regression methods [
4], neural networks [
5] and various simulation methods [
6]. The International Energy Agency (IEA) recognises two basic approaches to energy consumption and energy savings assessment, namely, “top-down” and “bottom-up” approaches, for which the IAE has provided detailed guidance for calculations [
7]. The “bottom-up” approach can be based on the physical properties of buildings, statistical models or may be a hybrid. In the EU, the “bottom-up” model is predominantly used when determining incentive legislative frameworks to achieve energy efficiency, but its main disadvantage is the fact that such models are inadequate in describing non-technical influential parameters and introduce a greater number of model assumptions related to behavioural aspects of energy consumption such as demographic factors, age of final consumers, daily disposition of space usage, consumer willingness to pay, etc. [
8].
Recently, an increasing research focus has been put on the aforementioned numerous non-technical factors affecting energy consumption. Thus, Yang and et al. consider the behaviour of the space user and the level of thermal comfort [
9]. Furthermore, Nguyen et al. analyse intelligent systems for monitoring the use and control of energy consumption in buildings [
10]. With the increase of available data on non-technical parameters of consumption and the development of data collection technologies, the analysis of these data has the potential to support a better understanding and modelling of energy consumption due to a series of non-technical factors. When analysing big data, statistical methods are used, such as Olofsson et al., using basic coordinate analysis and regression method of partial least squares to determine the main influencing factors for energy consumption in district heating systems, electricity consumption, potable water consumption and heat losses [
11]. Furthermore, machine learning algorithms, predominantly a support vector machine, are shown to be suitable for assessing energy consumption in buildings [
12,
13].
By changing the energy policy within the EU, district heating systems have been identified as one of the main instruments for achieving the goals of increasing energy efficiency and increasing the share of renewable energy sources in the energy mix [
14,
15]—indicating the need for precise methods of estimating consumption and saving energy in these systems. In February 2016, the European Commission issued the first strategy for optimization of heating and cooling in Member States, which illustrates the overall efficiency of these systems in the future [
16]. At the end of 2012, the European Parliament and the Council of the European Union adopted Directive 2012/27/EU on energy efficiency, the primary objective of which is to achieve energy savings of 20% by 2020 and to open new energy efficiency improvements after this period. Also, in Article 9 of the abovementioned Directive, introduction of individual distribution of heat energy consumption in district heating systems, has been identified as the basic prerequisite for achieving energy savings by changing user behaviour [
17]. Compared to the energy consumption in the general building stock, additional influencing factors in the district heating systems refer to the existence or absence of individual consumption measurement, the existence of DHW preparation or lack thereof, the possibility of replacing the primary energy source, the amount and management of heat losses in the distribution system, and valorisation of passive heat gains in multi-storey buildings [
18]. The estimate of the effect of individual measurements of heat energy consumption by using a heat cost allocator (HCA) was experimentally performed in the seventeen-year period by measuring consumption in two identical housing units (equally entering the same building) so that heat cost allocators are incorporated in one unit, while in the other they are not. The annual savings achieved in the unit with built-in heat cost allocators are about 27% [
19]. Such estimated savings cannot be uniquely attributed to the impact of installing heat cost allocators, because energy consumption is influenced by numerous factors. Previously, a great deal of research has been conducted in order to describe and understand the energy consumption in the residential sector. Most of the accessed categories of objectives are identified in these three categories: (i) energy, (ii) occupants’ behaviour and (iii) guidelines (in energy policies or simulation and design) [
20]. In this paper an emphasis is placed on occupant behaviour related data, and the questionnaire is made with the focus on this area. The questions about relevant parameters in the questionnaire and interviews conducted in this paper where based on previous research carried out in Denmark and the Netherlands [
21,
22]. As Croatia is located in southern Europe, the relevant parameters defined for the northern countries of Denmark and the Netherlands where double-checked with the parameters defined for Greece [
23].
The goal of this paper is to make an initial classification of influential factors on heat consumption in the buildings connected to district heating systems with cross-referencing parameters from both billing data and questionnaires on behavioural and demographic characteristic.
This paper is organised as follows:
Section 2 describes materials and methods used in this paper, while
Section 3 presents the results of the research performed. Finally,
Section 4 reports the discussion.
Research Background and Motivation
The Energy Efficiency Directive (2012/27/EU) was transposed into the Croatian legislation in the Heat Market Act (Official Gazette 80/13, 14/14 and 95/15) and the Energy Efficiency Act (Official Gazette no. 127/14). According to the mentioned acts, it was necessary to install electronic heat cost allocators (abbreviated HCA) or heat energy meters and thermostatic radiator valves in all residential/business premises connected to the district heating systems by the end of 2016.
Starting from 2010 in its new, energy legislation, based on EU principles and requirements regarding energy sector organisation and functioning, as well as promoting energy efficiency, the Republic of Croatia has opted for a model in which each residential area, regardless of whether they are multiapartment buildings or a family homes, has its own access to all network services and its own measurement of the service performed. For efficient energy use it is essential that the owner of the living space can decide on how energy is used so that his energy consumption can be measured and paid as much as it is spent. Prior to the introduction of individual allocation or of heat consumption measurement, actual consumption was measured on the building level and then equally allocated based on the floor area with no “energy-based” measurements on the apartments level.
However, most of the apartments that were constructed prior to 1995 and are connected the district heating systems (DHS) in buildings have a vertical distribution system, meaning that one vertical pipe goes through each room on each floor so the apartments do not have a single input/output point. In these apartments it was not economically viable to provide direct heat consumption measurement without a technically and financially demanding reconstruction of the heating installation within the building. For such cases the installation of HCA was allowed as a more economically feasible option.
HCAs are devices attached to individual radiators in buildings that allocate the total heat output of the individual radiator within apartment. For efficient energy use it is essential that the final consumer has an opportunity to measure the actual energy used in a specific unit and billed based on the actual consumption. Numerous studies and analyses have confirmed that by installing HCAs, annual heat savings at the heat station level (i.e., the entire building) can be expected at the level from 15% to 30% [
19]. The achieved savings, i.e. reduced heat consumption, are further distributed to each individual consumer by a certain allocation methodology. However, the installation of HCAs in Croatia caused great dissatisfaction for a number of final consumers. A total of 70 complaints of final customer submitted by four energy entities performing the activity of supplying heat to households and industrial subjects, i.e., the activity of the buyer and a consumer protection association, were analysed. The analysed complaints can be classified into the following three groups with regard to the subject of the complaint: complaints against the excessive bill for the delivered heat energy and preparation of hot water, requirements for information and dissatisfaction with the overall heat energy consumption calculation system.
The final motivation of this work presented in this paper is to develop a model that would be used for the purposes of assessing the effects introducing individual metering has had on the heat consumption in district heating, on household, building, network and national levels.
4. Discussion
Although most studies dealing with assessing energy savings after the introduction of individual metering (either by individual heat meters or heat cost allocators) in buildings connected to district heating systems have reported significant savings [
19], no comprehensive assessment of the savings at the level of an individual apartment has been made. At the same time, the experience in implementing the EU Energy Efficiency Directive in the part of individual metering in Croatia, has shown that a large number of final consumers did not achieve the presumed savings, but have even significantly increased their cost for heat. This fact was a motivation for conducting research into a comprehensive assessment on influential factors on consumption in buildings connected to district heating systems.
In this paper influential factors on the energy consumption in buildings connected to DH systems are defined as technical (mostly quantitative variables) and non-technical (mostly qualitative variables), whereby the non-technical forms of consumption include social and behavioural aspects, such as demographic factors, the daily schedule of the use of space and others. This represents the originality of this paper as most of the previous analyses and models, like the research done by Juodis [
27], predominantly delved into technical factors only.
When analysing the data comprising both technical and non-technical data, where technical data was available from the billing data and non-technical data was obtained by virtue of questionnaires, at this level of research it can be concluded that significant influential factors on heat consumption are the ratio between the metered values (for HCA these are impulses) and the heated area, window type, daily occupancy rate and frequency of ventilation.
Further research is planned based on the findings of this paper. A model for a typical building will be developed based on the qualitative data and merged with a large available database on heat consumption, as described in the Introduction.
Based on the model presented in this paper, the next step will be to implement machine learning algorithms and statistical methods together with comparative analysis and identification of the most appropriate algorithm to analyse the separate effect of energy efficiency measures installing individual measurements in buildings heated from heating systems to reduce energy consumption.
The final goal of this research is to develop a model that would be used for the purposes of assessment of the effects of introducing the individual metering on the heat consumption in district heating, on households, buildings, networks and at a national level.