Evaluation Study of Energy Performance and Conformity to Regulations for Ordinary and HEP Housings: Case Study Based on Measurements at Djelfa City, Algeria

Energy efficiency in buildings is a prime objective for energy policy at regional, national and international levels at the present time. The global energy consumption by residential and commercial sectors has exceeded other major sectors such as industrial and transportation and has steadily increased by 20 % to 40 % in developed countries [1]. Moreover, the energy demand is expected to be continue in the future because of several reasons as; growth in population, increasing demand for building services in addition to comfort levels, together with the rise in spent time inside buildings. According to the International Energy Agency IEA (2006) [2], consumption will be increased very likely for the growth in HVAC systems, where energy use is particularly significant (50% of building consumption). Statistically, the energy uses in residential sector estimated about 13 % of the total world delivered energy consumption in 2040. Delivered energy consumption in the residential sector grows by an average of 1.4 %/year from 2012 to 2040, with a total increase of 48 % over the entire period [3]. The International Energy Agency IEA (2006) has collected a scary data on energy consumption trends, where forecasts show that energy consumption in underdeveloped countries will exceed that of developed countries by 2020 with an average annual rate almost equal to 3.2 %. Besides, CO₂ emissions during the previous two seasons 1984-2004 are increased by 43 %, with an average annual increase of 1.8 % [2]. Consequently, the world is seriously concerned about the difficulties of supply, the depletion of energy resources and the heavy environmental impacts (depletion of the ozone layer, global warming, climate change, etc.).

In the last decades, energy efficiency of buildings is at the top of interest by different researchers. Multiple methodologies are invested in order to make the building less energy consuming. In the next section, a recall of some experiences and investigations will be addressed.

Statistical data on the socio-economic, constructive and climatic aspects gives a possibility of decision-making related to the actions of the policy-making and the modernization of the energy of the houses [4]. The statistics also help to improve the energy performance of buildings and redistribute heating costs more equitably, overcoming the contrast between the principles of accountability and equity [5].

Performed study by Elsharkawy and Rutherford (2018) showed how the awareness and behaviour of occupants influence energy consumption, according to a study done on 150 houses before and after energy retrofitting. Finally, the experience resulted a minimal energy saving, these results are largely attributed to people's well-established habits of energy use, higher comfort levels, lack of awareness, and inadequate information to help residents manage better their energy consumption after the renovation [6]. Calderón and Beltran (2018) conducted a two-year empirical study of the effects of insulation on a social housing building in Newcastle upon Tyne (United Kingdom). The results show that users are influenced by the benefits of energy efficiency like additional heat form rather than energy savings [7]. Cauvain and Karvonen (2018) explained their agency’s approach to advancing the carbon reduction agenda, starting with the influence of decision-makers and funders, as well the owners and occupants of the building. As a result, they were able to directly modify their housing stock, so they influenced their peers in the broader social housing sector [8].

Moreover, several experimental studies were realized in this field. For example, Kolokotroni et al. (2018) demonstrated that cold paint on the roofs of low-income homes in solar-intensive areas will significantly improve thermal comfort through natural ventilation and reduce the demand for cooling energy [9]. Sánchez-Reséndiz et al. (2018) have illustrated the behaviour of living walls and their beneficial effects in semi-arid environments of central Mexico. The results show that the design with such a wall contributes to the phase shift (loss/gain) of heat of the enclosure, increase the green surfaces and promote social interactions [10]. Liu et al. (2018) presented a hybrid solar heating system including a solar kang system, a Trombe wall and a direct gain window for a building located on the Qinghai - Tibetan Plateau. The study outcomes show that these systems play an important role in improving comfort and indoor temperature [11].

Control strategies and technologies can be a useful tool in energy optimization. Seeam et al. (2018) demonstrated that the use of a building energy management system (BEMS) can save energy by 50 % in terms of reducing heating time compared to scheduling [12]. The cloud-based system can be connected to home energy management systems (e.g. Wi-Fi smart plugs and wireless temperature sensors), these recent systems that make a smart and technological home, actually are considered as a cheap and affordable [13].

On the other hand, insufficient understanding of heating and ventilation equipment and the lack of proper commissioning of heating and mechanical ventilation with heat recovery (MVHR), leads to overconsumption of energy and affects the quality of the air [14].

Also, Zahiri and Elsharkawy (2018) have studied the correlations between the patterns of occupancy and energy consumption, thermal comfort and energy performance of buildings. Their results confirm that the socio-demographic characteristics, the habits of the occupants as well as the insufficient thermal envelope have a considerable influence on their theories [15]. Karatasou et al. (2018) have shown that high electricity consumption and high energy consumption for household space and water heating are related to different factors. Firstly, The high electricity consumption is related to the socio-demographic characteristics of the occupants and the nature of the building (such as household size, number of bedrooms, total floor area, building age and the existence of the electric water heating system).On the contrary, high-energy consumption for space and water heating is only related to building characteristics [16]. San Miguel-Bellod et al. (2018) indicates that housing consumption is related to socio-economic characteristics, building systems and heating systems, the result is based on a study carried out on 112 dwellings built in 40s and 80s in Spain. It shows that households connected to district heating networks maintained indoor air temperatures above 18 °C on a continuous basis. Whereas 54 % of households outside these heating systems had indoor air temperatures below this threshold at night in the bedrooms. While 25 % had temperatures below 18 ° C during the periods occupied during the day and the night [17]. Gonçalves et al. (2018) studied the combined effect of thermal inertia, exterior shading and controlled natural ventilation on thermal comfort in hot areas, based on their experience and the strategy adopted, they were able to record stable indoor thermal conditions. The air temperature oscillating between 24 °C and 26 °C when the outside temperature reached 32 °C [18]. Colcough et al. (2018) identified the potential of PH (passive house) typology as a solution to nZEB by investigating, construction costs, indoor environmental conditions, energy consumption, and heating and ventilation costs [19]. Ioannou et al. (2018) presented by real-time in situ measurements of thermal comfort and perception of thermal comfort in 17 residential dwellings in the Netherlands, the results show that if indoor temperatures were within the comfort bandwidth of the adaptive model, Occupants have often reported feelings of comfort to there than neutral. The results explain an ambiguity and that they are an indiscrimination between the different levels of thermal sensations or that allihésie plays a role and that the neutral sensation is not comfortable or that many actions take place by habit and not in the aim to improve thermal comfort [20].

Furthermore, few studies used survey methodology to investigate occupant perceptions and behaviours in social housing settings [14], through survey method, Pérez-Fargallo et al. (2018) evaluated the degree of adaptive comfort in housing. It is possible as result to apply international standards to social housing for low-income families [21]. DellaValle et al. (2018) identified a range of context-specific variables that can be used as levers to align behaviour to retrofit interventions. For example, ventilation behaviour is recognised as a social practice and scarce level of education affect him [22].

The building simulation approach is also used in other studies to examine the effect of technical, behavioural and urban design interventions [14]. Souliotis et al. (2018) presented two innovative solar water heating systems integrated on the facades (Hybrid Photovoltaic/Thermal (PV/T) solar systems) and the roof (Integrated Collector Storage (ICS) solar water heaters) of a social housing building in the municipality of AgiosDometios, Nicosia, Cyprus and is evaluated through Life Cycle Assessment. The results show that the thermal behaviour of the building is improved by 10 %, while the electric hot water and space heating requirements have been reduced by up to 80 % and 50 %, respectively [23]. Petidis et al. (2018) studied the existing building of the student hall of the Technical University of Crete, by method of help of surveys and simulations. They used five energy reduction scenarios, namely thermal insulation of the envelope, green roofs, LED bulbs, window replacement and a combination of all were chosen, the scenario resulted in a reduction of up to 36 %. The photovoltaic of the roof made it possible to save 62 % compared to the initial energy consumption, a very satisfactory result for the energy performance objective target of near-zero [24]. In the project of Santangelo et al. (2018) they explored the role of occupant behaviour modelling in assisting decision-makers in designing home renovation strategies. The authors recommend that for retrofit strategies to be effective, appropriate informative tools must be developed at an early stage to promote behavioural change for responsible energy use [25]. Sosa et al. (2018) investigated the thermal behaviour and energy consumption of different urban scenarios for low-density social housing neighbourhoods in Mendoza, Argentina. The results show that, Energy savings depend on a suitable layout and street orientation, urban trees, and the albedo of the building materials [26].

Policy development, technological advancement and behavioural changes should go hand in hand for achieving energy efficiency and comfortable conditions for the low income household sector [14].

In 2008, Algeria initiated a pilot program of 1500 HEP housing, for the purpose to keep up with the current world thought approach, this program is divided on several cities. Djelfa has benefited from 80 housing, and the approach is to build housings with energy efficient.

The methodological advance in the current study can be summarized; from one hand, assessment of two housings different by four methods. One of the two housings is built according to the so-called principle HEP and the other is ordinary housing. The methodology adopted for the analysis is as follows; calculation note, field’s measurements, building simulation and occupant survey. Besides, the present study evaluating the Algerian government's decision on the techniques used and the financial process.

During the current study a week period in the early January 2018 is chosen (from 12 to 18). The selected duration is considered by the MNO station as the coldest period in the region during 2018 (Cf.Figure 1) [34]. Moreover, two housings types were taken for the thermal comfort analysis, HEP housing and Ordinary housing.

3.1 Energy consumption using experimental and survey analysis

According to EIA (2016), the energy consumption in the residential sector comprises all energy consumed by households, excluding transportation uses. It includes energy used for heating, cooling, lighting, water heating, and consumer products. Energy consumption in the residential sector is affected by income levels, energy prices, location, building and household characteristics, in addition to weather, efficiency and type of equipment, energy access, availability of energy sources, energy-related policies and so on. Consequently, the energy type and amount consumed by households can vary significantly within and across regions and countries [3].

Table 1. Global questions about HEP & Ordinary housing families

Concerning the consumed energy analysis from the electricity and gas in case of the two housings types, the research is based on:

• The analysis by survey method based on the composition of the two families, in which the HEP family consists of two parents and two grandchildren (<5years), and the ordinary housing family is composed from two parents and four children with  different ages(<5, >5<10, >10<15, >15) (Table 1)
• The experimental analysis of the gas consumption is carried by the verification of the consumption quantity before and after the experiment (January 12-18). So the survey method gives us a vision on all the instruments of the two families that works with gas and checking their operating times and power during the use.

In the electrical energy consumption case, the same approach as the gas consumption is adopted.

3.2 Simulation

The objective of this step is to visualize and analyse by simulation the thermal comfort in a built space (Table 2). The model Autodesk Ecotect (2011) offers a wide range of thermal performance analysis features [29].

The operating conditions of the used model are well affected by several parameters such as: Internal design conditions; Occupancy and operation; HVAC and Material assignments for modelling.

Table 2. Simulation protocol for HEP & Ordinary housings

3.3 Calculation and reference regulations

The used reference equations in the theoretical methodology are made according to the RTD C3-2 of the Algerian regulations. The regulatory requirement on which this RTD is based is by setting a threshold that should not be exceeded to limit building’s energy losses. Respecting this threshold should allow saving from 20 to 30 % of the global energy consumption for the housing heating [30].

Objectives of the RTD application can be cited as follow; Determination of buildings heat losses, Verification of buildings compliance with thermal regulations, Dimensioning of buildings heating systems and thermal design. Present method is divided into two stages; the first step is to calculate the reference losses D_ref. While the other step is calculating the actual heat losses D_t about walls, floor, windows and doors.

After the calculation of the reference heat losses D_ref  ( $W / °C$) and the real heat losses D_t ($W / °C$) of different surfaces, the regulation requires that: D_t <D_ref  with a tolerance of 5 % overrun, should be respects the following formula.

$D_{t} / D_{r e f}<1.05$   (1)

3.3.1 Reference losses

This step permits to define the regulatory threshold that should not be exceeded:

$D_{\text {ref}}=a . S_{1} .+b . S_{2}+c . S_{3}+d . S_{4}+e . S_{5}$   (2)

With $S_{1}, S_{2}, S_{3}, S_{4}, S_{5}$.  represent the surfaces of the upper floor, lower floors, outer walls and the outer doors & glazing, respectively.

And the coefficients a, b, c, d, e attribute to the region of Djelfa.

$D_{T}=\left(D_{p r}\right)_{i}+\left(D_{p l h}\right)_{i}+\left(D_{p v}\right)_{i}+\left(D_{p}\right)_{i}+\left(D_{l n c}\right)_{i}$   (3)

3.3.2 Real losses

Heat losses through the outer walls:

The lost heat through the exterior walls is computed with the next formula:

$\left(D_{p r}\right)_{i}=\sum\left[\left(k_{o w} \times A_{o w}\right)+\left(K_{o w} \times L_{o w}\right)\right]$   (4)

where outer walls’ coefficients are;

k_ow: Surface Thermal transmission coefficient $\left(W / m^{2} . °C \right)$, A_ow: Area (m²), K_ow: Linear thermal bridges $\left(W / m. °C\right)$ and L_ow: Length of thermal bridges (m).

Heat losses through the roof:

The lost heat through the high can be computed, using the following expression:

$\left(D_{p l h}\right)_{i}=\sum\left[\left(k_{r} \times A_{r}\right)+\left(K_{r} \times L_{r}\right)\right]$   (5)

where the roof’s coefficients are;

k_r: Surface thermal transmission coefficient $\left(W / m^{2} . °C \right)$, A_r: Area (m²), K_r: Linear thermal bridges $\left(W / m. °C\right)$, and L_r: Length of thermal bridges (m).

Heat losses through the windows:

In the windows case, the heat losses are determined as follow:

$\left(D_{p v}\right)_{i}=\sum\left[\left(k_{w} \times A_{w}\right)+\left(K_{w} \times L_{w}\right)\right]$   (6)

where windows’ coefficients are;

k_w: Surface thermal transmission coefficient $\left(W / m^{2} . °C \right)$, A_w: Area (m²), K_w: Linear thermal bridges $\left(W / m. °C\right)$, and L_w: Length of thermal bridges (m).

$R=\frac{1}{k_{w}}=\frac{1}{k_{v n}}+r_{v}+r_{r i d}+r_{o c c}$   (7)

where,

k_vn  $\left(W / m^{2} \cdot °C\right)$ is the thermal conductivity of glazing (conventional table);

r_v  $\left(m^{2} . °C / W\right)$ is Additional thermal resistance of the potential sheers, $r_{p}=0,025 m^{2} \cdot °C / W$;

r_rid  $\left(m^{2} . °C / W\right)$ is the thermal resistance of possible curtains, $r_{r i d}=0,03 m^{2} . °C / W$ ;

r_occ  $\left(m^{2} . °C / W\right)$ is the thermal resistance of occultation (no occultation).

Heat losses through exterior doors:

$\left(D_{p}\right)_{i}=\sum\left[\left(k_{e d} \times A_{e d}\right)+\left(K_{e d} \times L_{e d}\right)\right]$   (8)

where exterior doors’ coefficients are;

k_ed: Surface transmission coefficient $\left(W / m^{2} \cdot °C\right)$, A_ed: Area (m²), K_ed: Linear thermal bridges $\left(W / m. °C\right)$, and L_ed: Length of thermal bridges (m).

Heat losses through unheated walls:

$D_{l n c}=Tau\left(\sum\left[\left(k_{u w} \times A_{u w}\right)+\left(K_{u w} \times L_{u w}\right)\right]\right)$   (9)

where unheated walls’ coefficients are;

k_uw: Surface transmission coefficient $\left(W / m^{2} \cdot °C\right)$, A_uw: Area (m²), K_uw: Linear thermal bridges $\left(W / m. °C\right)$, and L_uw: Length of thermal bridges (m).

$T a u=\frac{t_{i}-t_{n}}{t_{i}-t_{e}}=\frac{d_{e}}{d_{e}+a_{c}}$   (10)

1. t_i (°C) is the indoor temperature.
2. t_n  (°C) is the temperature of the unheated space.
3. t_e  (°C) is the outside temperature.
4. a_c  (W/ °C) Represents the heat gains of various heated rooms to the unheated room.
5. d_e (W/ °C) Represents the heat losses of the unheated room to the outside.

In the present project, experiment, theoretical, numerical and survey investigations were performed in order to study the energy performance and conformity to regulations for two different types of housings; ordinary and HEP housings, by taking Djelfa city (Algeria) as a case study.

According to the literature, researchers provide many strategies to be applied in order to improve the energetic performances of the residential sectors. It is well known that various process should be respected while the conception and realization of buildings, such as:

• Architectural and urban design by orienting housings to the south for maximum capture during the winter, this orientation also facilitates protection against the sun's rays during the summer [27, 28]. This hypothesis is also proved by the research of Sosa et al. (2018) and Montarry (2005), where they reflect that the architectural and urban design is the primary element for a better energy efficiency [26, 32]. Also energy savings in addition to the thermal comfort are depends on a suitable layout and street orientation, urban trees, and the albedo of the building materials.
• The outer envelope such as walls, floor, windows and loggias doors in addition to the limit of thermal bridges. Current design choice is proven by numerous researches, where the effect of the building envelope performances on the energy efficiency is proved [15-19, 24, 26].

A cold region for more than six months a year, in addition to its high seasonal variation of temperature min and max is selected in this study. Two building types are considered for examination (HEP housing and ordinary housing), the mentioned housings are located in two different sites with the same architectural context (orientation & disposition in elevation).

Findings from experiment (real measurements) demonstrate that only 1.3 % increase is recorded in the gas consumption for ordinary housing compared to HEP housing. While the computation based on the Algerian thermal regulatory show that the HEP housing can save up to 48 % of heating energy compared to ordinary housing, which explain clearly the big difference between the real findings and what should be found. Besides, the difference in energy consumption in electricity is 35 % saving in favour of the HEP housing based on the experiment results. This gain is mainly due to the use of economical electric instruments in buildings with high energy efficiency HEP.

Based on the mentioned scenarios, the Algerian government did not really invest much in this pilot project, the financial impact compared to ordinary housing is estimated at only 187500 DA (1581,28 USD) per surface housing of 69m². However, many solutions can be applied at the same time to have optimal energy performance such as; the use of solar panels (photovoltaic or hot water) installed on the facades or on the roofs, trompe wall, kang system, living wall, ect… In addition, other materials and techniques can be used to improve thermal and sound insulation or increase natural ventilation in homes in order to have a comfortable and healthy indoor environment with less energy consumption [14, 24, 23, 31, 33].