• Energy Research

Abstract An estimation of passive cooling techniques was conducted for 14 cities in Brazil, using a fairly accurate algorithm that accounts for heat conduction, convection, radiation, and evaporation; this was done to determine the amount of heat gain/loss of room air, defined as a particular quantitative index for passive techniques. Heat gains and losses were calculated for four envelope conditions – namely, insulated, high-albedo, wet surface, and a combination of the previous two – and compared to a case assumed to be the standard condition. A conclusion drawn was that a passive design is efficient in decreasing the need for cooling in typical dwellings in Brazil; solutions should differ with regional climate characteristics. In semi-arid areas, evaporative cooling showed the best results. Reduced heat gain was found during the warm seasons for all cities, along with increased heat gain during the cool seasons for mid-latitude cities. In particular, a combination of high-albedo enveloping and evaporation can greatly decrease heat gain in building walls. High-albedo surfaces in the sub-tropical areas found in southern Brazil are more efficient. It is suggested that passive techniques should be conceived in such a way so as to work during the cooling season and be disabled during mild ones.

Software for numerical simulation of various types of energy used in buildings, i.e. building energy simulation (BES), have become an essential tool for recent research pertaining to building physics. TRNSYS is a well-known BES used in both academia and the construction industry for a wide range of simulations, such as the design and performance evaluation of buildings and related facilities for heating, cooling, and ventilation. TRNSYS has a modular structure comprising various components, and each component is interconnected and compiled through a common interface using a FORTRAN compiler. Its modular structure enables interactions with various external numerical simulation tools, such as MATLAB, Python, and ESP-r. For ordinary simulations of building energy load using TRNSYS, the generic module Type 56 is usually recommended, which provides detailed physics modelling of building thermal behaviours based on unsteady energy conservation equations and Fourier’s law for each building material. However, Type 56 explicitly depends on the transfer function method to discretise the original differential equations; therefore, it cannot model nonlinear phenomena, such as latent heat and moisture transfer between a building surface and ambient air. In other words, the current TRNSYS cannot be used to estimate the effectiveness of evaporation during cooling, which is a typical passive design method. Hence, the authors developed a MATLAB/TRNSYS integration scheme, in which TRNSYS was modified to model simultaneous heat and moisture transfer from the wet roof surface of a building. This scheme enabled TRNSYS to calculate the rate of evaporative heat and moisture transfer dynamically from the roof surface, assuming a control volume approximation of the roof surface. Finally, the effect of evaporative cooling on the thermal performance of an Indian building was estimated using the modified model.

Abstract Although various cooling approaches have been proposed to overcome the thermal discomfort in residential buildings, tropical developing countries still lack affordable and effective retrofitting methods. The objective of this study was to evaluate the effectiveness of an affordable retrofitting method with high-density polyethylene (HDPE) nets as roof covers for shading over the roof, supported by full day free-running ventilation, and heat insulation above the ceiling of residential buildings in hot–humid climate regions to overcome the thermal discomfort. Field measurements were carried out in a corner terrace house in Malaysia, from September to December 2018. The roof cover with HDPE nets maintained a consistent surface temperature at the roof tiles and reduced the convective heat flux by approximately 70–80% in the attic and 88% in the room. Further, it improved the compliance (acceptability: 80%) of the whole-day mean operative temperature in the room (hot–humid climate) by 10%. The roof cover can effectively provide thermal comfort in residential buildings in Malaysia, which has a hot–humid climate. Alongside active cooling with the ceiling fan, required comfortable indoor temperature can be reached under the hot–humid climate, particularly during the night-time. Furthermore, the zero-energy-consuming, low-cost low-technology roof cover method is very suitable for low-cost houses with roof tile in hot–humid climate regions.

The computer software AUSSSM TOOL, originating from the methodology of the revised-architectural-urban-soil-simultaneous simulation model (revised-AUSSSM), was developed by adopting the graphical user interface (GUI) features to support users, who can use the interactive computer display for parameter settings, simulating, visualizing, and reporting the numerical calculation results instead of complicated programming. The purpose of the AUSSSM TOOL is to determine quantitative parameters such as air temperature, exhaustive heat from air conditioning systems, energy heat balance, etc. within the urban canopy structure, which data enables the evaluation of effects of urban heat island (UHI) in concrete terms useful to urban planners, architects, engineers, and so forth in the field of urban climatology involving building scale. In addition to conducting a full numerical simulation, in order to simplify a comparison among complex factors influencing UHI, numerical experiments based on Taguchi design of experiment theory (DOE) were carried out. The results of the numerical experiments were stored in a database and ready to be instantly grasped by any inexperienced user corresponding to their specified conditions. This paper describes the fundamental method of the revised-AUSSSM, the objectives of related software development, and the structures of the AUSSSM TOOL and the techniques comprising its algorithm to present the numerical simulation results in particular.

The aim of this study is to determine preferences for the environmental factors of residential buildings by using two different methods: the conjoint analysis and ranking method. We tried to identify consumers' monetary value regarding environmental performance by testing their Marginal Willing to Pay (MWTP). A survey was conducted in Seoul, Korea to clarify the preference and monetary value of four selected attributes representing environmental performance. These attributes are reduction of energy bills, reduction of CO2 emissions, reduction of volatile organic compound emissions, and application of information technology facilities. The result can be summarized as follows. The MWTP for 1% reduction of CO2 emission is estimated about $377 USD, being 2 times higher than that for reduction of VOC emissions and almost same as that for the reduction of energy bills. The energy bill is most preferred and IT facilities are least preferred in ranking method. Preferences vary according to respondents' socio-demographic factors and the numerical information in conjoint analysis makes strongly reflect them.

With rapid urbanization, massive amount of energy is required to compensate the electricity usage thus calls for a need to Malaysian government issuing standard MS1525:2014 for temperature settings in office buildings to meet energy efficiency goal. In co-sharing spaces, personal thermal comfort is often not met due to the different thermal sensation at different location inside office rooms. This study was conducted at four postgraduate office spaces with cooling mode in university campus located at Kuala Lumpur to evaluate the occupant’s thermal sensation. We used different set-point temperature of air conditioning ranging from 18.0°C to 28.6°C. The indoor thermal variables such as air temperature, globe temperature, relative humidity, and air velocity are measured at each respondent’s workspace and 200 responses were recorded from ten subjects. The mean value of thermal sensations votes is -0.4 and were within comfort range. 76% of responses voted ‘neutral’ humidity sensation as occupants have adapted to humid condition in Malaysia. The comfort operative temperature found in this study is 24.9°C which indicates that the minimum recommended temperature for energy conservation did not deprive occupants from comfort.

Owing to the high energy demand of buildings, which accounted for 36% of the global share in 2020, they are one of the core targets for energy-efficiency research and regulations. Hence, coupled with the increasing complexity of decentralized power grids and high renewable energy penetration, the inception of smart buildings is becoming increasingly urgent. Data-driven building energy management systems (BEMS) based on deep reinforcement learning (DRL) have attracted significant research interest, particularly in recent years, primarily owing to their ability to overcome many of the challenges faced by conventional control methods related to real-time building modelling, multi-objective optimization, and the generalization of BEMS for efficient wide deployment. A PRISMA-based systematic assessment of a large database of 470 papers was conducted to review recent advancements in DRL-based BEMS for different building types, their research directions, and knowledge gaps. Five building types were identified: residential, offices, educational, data centres, and other commercial buildings. Their comparative analysis was conducted based on the types of appliances and systems controlled by the BEMS, renewable energy integration, DR, and unique system objectives other than energy, such as cost, and comfort. Moreover, it is worth considering that only approximately 11% of the recent research considers real system implementations.

Remote work (working from home) became a norm rather than an exception for the global workforce during the COVID-19 pandemic, influencing every facet of life in both positive and negative ways. The stringent action of the Malaysian government in enacting the Movement Control Order (MCO) motivated the investigation of its impact on the energy consumption behaviour of working people regarding air-conditioner (AC) use. To this end, this study conducted a cross-sectional survey through an online platform. An ordinal logistic regression model (ORL) was used to analyse the collected data of 1873 respondents to determine the factors influencing the ordinal variable of interest, AC-usage behaviour during remote work. Next, the variable with unordered categories, the MCO-induced change in AC-usage behaviour, was analysed using a multinomial regression model (MLT) to identify the potential determinants. Finally, a reason analysis unveiled aspects behind the transition in AC use during remote work. This study identified stopping AC use during remote work despite using it at the office before the MCO period as the most significant change in AC-usage behaviour due to MCO. This change was frequently adopted by people with medium-level incomes and high electricity bills. By contrast, participants unfamiliar with their electricity bill were most likely to start AC use during remote work, although they did not use it before the MCO. Participants working remotely in the communal spaces of their houses preferred to stop using ACs during MCO compared to private room users. Furthermore, age group and ethnicity significantly influenced AC-usage behaviour in remote work and changes in such demeanours. These findings recommend policy interventions to expedite limited AC use for a sustainable energy sector, even during future climatic emergencies .

Modern power grids face the challenge of increasing renewable energy penetration that is stochastic in nature and calls for accurate demand predictions to provide the optimized power supply. Hence, increasing the self-consumption of renewable energy through demand response in households, local communities, and micro-grids is essential and calls for high demand prediction performance at lower levels of demand aggregations to achieve optimal performance. Although many of the recent studies have investigated both macro and micro scale short-term load forecasting (STLF), a comprehensive investigation on the effects of electrical demand aggregation size on STLF is minimal, especially with large sample sizes, where it is essential for optimal sizing of residential micro-grids, demand response markets, and virtual power plants. Hence, this study comprehensively investigates STLF of five aggregation levels (3, 10, 30, 100, and 479) based on a dataset of 479 residential dwellings in Osaka, Japan, with a sample size of (159, 47, 15, 4, and 1) per level, respectively, and investigates the underlying challenges in lower aggregation forecasting. Five deep learning (DL) methods are utilized for STLF and fine-tuned with extensive methodological sensitivity analysis and a variation of early stopping, where a detailed comparative analysis is developed. The test results reveal that a MAPE of (2.47–3.31%) close to country levels can be achieved on the highest aggregation, and below 10% can be sustained at 30 aggregated dwellings. Furthermore, the deep neural network (DNN) achieved the highest performance, followed by the Bi-directional Gated recurrent unit with fully connected layers (Bi-GRU-FCL), which had close to 15% faster training time and 40% fewer learnable parameters.