Optimization of insulation thickness of external walls of residential buildings in hot summer and cold winter zone of China

It is important to reduce primary energy consumption and greenhouse gas emissions associated with residential buildings in the hot summer and cold winter (HSCW) zone of China. Changing the insulation thickness of the external walls of residential buildings (ITEWB) is regarded as an effective way to manage such problems within a budget. This paper aims at developing an innovative way to select the optimal insulation thickness of external walls for residential buildings (OTWRB) in the HSCW zone of China, considering economic, energy and greenhouse gas emissions issues associated with the ITEWB. Four different cities and two different operation modes of the air conditioners (continuous and intermittent) are considered in this study. To explain the selection process, typical hypothetical buildings are simulated in Wuhan, Changsha, Hangzhou and Chengdu. Expanded polystyrene is chosen as the material of the insulation layer while split air conditioners are selected as the equipment for space heating and cooling. Integrated Environmental Solutions-Virtual Environment is used for the dynamic operational energy consumption of buildings. Life cycle cost method is adopted to calculate the economic impact of ITEWB on building performance. The Chinese life cycle database is used to quantize the impacts of ITEWB on building performance in the aspect of energy and greenhouse gas emissions based on the life cycle theory. The most appreciated insulation thickness is chosen from the thickness range of 30 mm to 150 mm. We find that for continuous operation mode of air conditioners in Wuhan, the optimal economic insulation thickness is 70 mm, whereas when considering only energy and environmental aspects, the OTWRB is 150 mm. These are all larger than the current insulation thickness which is 30 mm. When the weighting efficiencies of the economy, energy, and greenhouse gas emissions are different, the OTWRB varies from 70 mm to 150 mm for continuous operation mode. The different cities have little influence on the OTWRB while the different operation modes of air conditioners have some influence on the OTWRB

Social practices required for the recovery of cassava waste for heat generation in Thailand

Thailand is a dominant supplier of cassava to world markets, supplying around 67% of the global market, resulting in abundant cassava waste. However, farmers typically discard this waste in cassava fields, and thus it is underutilised as an energy resource. In addition, Thailand’s domestic cassava-based bioethanol plants depend on imported coal to produce heat. To address this challenge, this research investigated the potential for the collection and recovery of cassava waste from farms. Semi-structured interviews were conducted with Thai cassava farmers. Social practice theory was applied to evaluate the effects of cassava waste collection on Thai cassava farmers’ current agricultural practices. Three Thai cassava agricultural activities— land preparation, fertilization application and waste management—would be impacted by this new strategy. The connections between each of these potentially affected activities has been discussed and, ultimately, cassava waste collection by Thai cassava growers was proposed as a new practice. This study concluded that the enhanced use of cassava waste for the production of heat and power could potentially help Thailand meet its renewable energy targets in future

A review of existing building benchmarks and the development of a set of reference office buildings for England and Wales

The modern built environment has become more complex in terms of building types, environmental systems and use profiles. This complexity causes difficulties in terms of optimising buildings energy design. In this circumstance, introducing a set of prototype reference buildings, or so called benchmark buildings, that are able to represent all or majority parts of the UK building stock may be useful for the examination of the impact of national energy policies on building energy consumption. This study proposes a set of reference office buildings for England and Wales based on the information collected from the Non-Domestic Building Stock (NDBS) project and an intensive review of the existing building benchmarks. The proposed building benchmark comprises 10 prototypical reference buildings, which in relation to built form and size, represent 95% of office buildings in England and Wales. This building benchmark provides a platform for those involved in building energy simulations to evaluate energy-efficiency measures and for policy-makers to assess the influence of different building energy policies

An environmental impact assessment of the management of cassava waste: a case study in Thailand

In Thailand, cassava waste is one of the main biomass residues and has the potential to be used as a biomass fuel. However, currently most cassava waste in Thailand is left in agricultural fields or burnt on site and is not utilised for any energy-related purposes. This research investigates the environmental impacts associated with three cassava waste management practices including (i)- ploughing the waste to the soil (ii)-burning the waste in the field (iii)- collecting and using the waste in cassava-based bioethanol plant. The environmental impact assessment and material flow analysis associated with these management practices were conducted using the Global Emissions Model for Integrated Systems (GEMIS) package [1]. The outcomes of this study reveal that the CO2 emissions associated with these waste management practices are about 0.195, 0.243 and 0.361 kg CO2-eq/kg of as received (wet) cassava waste, respectively. Compared to other cassava waste disposal methods such as ploughing and burning, cassava waste collection would result in the biggest environmental impact, emitting nearly 85% more GHGs than ploughing and 48% more than burning

A fuzzy multiple attribute decision making tool for HVAC&R systems selection with considering the future probabilistic climate changes and electricity decarbonisation plans in the UK

Buildings account for 40% of total energy consumption in the UK and more than 55% of this energy is used by heating, ventilation, air-conditioning and refrigeration (HVAC&R) systems. This significant energy demand and the ascending trend in utilising HVAC&R systems together with the global need to impose energy-efficiency measures underline the importance of selecting the most appropriate HVAC&R system during the design process. This paper reviewed and classified a broad range of principal multiple attribute decision making methods. Among them, the fuzzy multiple attribute decision making approach was adopted to develop a decision making tool for HVAC&R systems selection. This was mainly due to the ability of this method to deal with the uncertainties and imprecisions of the linguistic terms involved in the decision making process. In order to make a decision on HVAC&R systems selection, 58 alternative systems, including both primary and secondary parts, were examined. The scope of this study enabled the consideration of all 18 climate regions in the UK and included the effects of climate change. In addition, the Government’s electricity decarbonisation plans were integrated within the developed decision making model for HVAC&R systems selection in office buildings in the UK. Finally, the model was transferred into a computational tool with a user-friendly interface

Decision-making on HVAC&R systems selection: a critical review

Buildings account for more than 40% of total energy consumption in most countries and more than 55% of this energy is used by heating, ventilation, air-conditioning and refrigeration (HVAC&R) systems. This significant energy demand, together with the global need to impose energy-efficiency measures, underlines the importance of selecting the most appropriate HVAC&R system in the early stages of a design process. However, this state-of-the-art study reveals that there is no review paper available in the open literature to critically analyse the existing methods for HVAC&R systems selection. Therefore, the aim of this paper is to critically review the body of knowledge on the adopted approach for HVAC&R systems selection. Based on the comprehensive literature review, the needs and gaps in this field are identified. It is revealed that the integration of probabilistic climate changes into the decision-making processes is one of the main areas that should be addressed in future studies. In addition, reliability and Life Cycle Cost of the systems, health and well-being, occupants’ satisfaction and indoor air quality are of paramount factors that should be taken into account in the decision-making process for HVAC&R systems selection

Influence of evaporative cooling by urban forests on cooling demand in cities

Trees provide important ecosystem services to urban human society. Their absence can lead to more pronounced environmental and social consequences, for example the urban heat island effect. Evapotranspiration (Et) from trees reduces air temperature in the urban microclimate by converting sensible heat to latent heat. Quantification and valuation of the ecosystem services provided by urban trees is important for improving cost-benefit evaluations in support of protecting tree planting and maintenance budgets and, thus, for building climate change resilience into cities. Inclusion of Et cooling could improve ecosystem service valuation models by producing a more complete picture of the benefits that urban trees provide to society. This study explores two approaches for evaluating climate regulation as an ecosystem service of urban trees. Firstly, an enthalpy-based approach was adopted to valuate latent heat of evaporation from tree transpiration (in three case study urban forests) by equating it to an equivalent service from an active direct evaporative cooling system. Secondly, energy savings to air-conditioned buildings was modelled using TRNSYS and TRNFLOW simulation programs with and without air precooled and humidified by urban trees. Trees are shown to provide substantial urban cooling with an annual valuation of £84 m estimated using the enthalpy-based approach, or ranging from £2.1 m to £22 m using TRNSYS and TRNFLOW dynamic simulation programs; both for inner London case study. The latter savings arose from a modelled 1.28 – 13.4% reduction in air-conditioning unit energy consumption. Challenges around assumptions of homogeneity in both built form and urban forest canopy effects are discussed. The case study examples highlighted differences in Et cooling between tree species, with Castanea sativa, Prunus avium, Quercus petraea, Platanus hybrida and Fagus sylvatica typically providing more Et cooling than any of the other tree species commonly found in urban forests. The research highlighted a shortage of published Et data, particularly for urban environments

Identifying occupancy patterns and profiles in higher education institution buildings with high occupancy density – a case study

Building occupancy patterns are an important factor in considering the energy efficiency of buildings and a key input for building performance modelling. More specifically, the energy consumption associated with heating, cooling, lighting, and plug load usage depends on the number of occupants in a building. Identifying occupancy patterns and profiles in buildings is a key factor for the optimisation of building operating systems and can potentially reduce the performance gap between the planning stage and the actual energy usage. This study aims to identify the patterns and profiles of the occupants in a selected case study building in England. In this study, occupancy data were collected over 12 months at five minutes intervals. A sensor was used to obtain high accuracy occupancy data compared to previous studies that encountered uncertainties in data collection. A set of clustering analyses was carried out to identify occupancy patterns and profiles in the building. The results of this study identified three different occupancy patterns and profiles as well as four drivers that influenced the occupants in the case study building: the beginning of the academic term, the examination period, the weekday/ weekends, and the vacation driver

Characterising the energy performance of centralised HVAC&R systems in the UK

Heating, ventilation, air conditioning and refrigeration (HVAC&R) systems account for more than 60% of the energy consumption of buildings in the UK. However, the effect of the variety of HVAC&R systems on building energy performance has not yet been taken into account within the existing building energy benchmarks. In addition, the existing building energy benchmarks are not able to assist decision-makers with HVAC&R system selection. This study attempts to overcome these two deficiencies through the performance characterisation of 36 HVAC&R systems based on the simultaneous dynamic simulation of a building and a variety of HVAC&R systems using TRNSYS software. To characterise the performance of HVAC&R systems, four criteria are considered; energy consumption, CO2 emissions, thermal comfort and indoor air quality. The results of the simulations show that, all the studied systems are able to provide an acceptable level of indoor air quality and thermal comfort. However, the energy consumption and amount of CO2 emissions vary. One of the significant outcomes of this study reveals that combined heating, cooling and power systems (CCHP) have the highest energy consumption with the lowest energy related CO2 emissions among the studied HVAC&R systems

Assessing and comparing a DDPG model and GA optimization for a heat and power virtual power plant operating in a power purchase agreement scheme

This paper proposes a deep deterministic policy gradient (DDPG) model for the operation management of a solar power-based virtual power plant (VPP) having a PPA with the grid and supplying power and thermal energy to consumers. The VPP serves to balance the solar power intermittency, cover the demand whenever solar power is absent, and ensure an efficient supply of energy. The literature in this field has introduced optimization algorithms to determine the power plant’s output power or heat on a rolling-horizon basis. Using the function approximation category, which involves reinforcement learning with neural networks, to solve the simultaneous thermal and power operation management of VPPs is still not well developed. The challenges imposed in this model are sourced from the non-linearity of the CCHP, the power and thermal balance constraints, and the consideration of continuous variables rather than discrete ones. A case study is simulated in Egypt to assess and compare the models. Compared to the genetic algorithm optimization, the proposed DDPG model achieved 3% more profit, 12% higher carbon dioxide (CO2) emissions, and 9% lower natural gas consumption. The DDPG solution was 57% faster than the GA. The results of the DDPG model proved that machine learning methods could outperform optimization in terms of optimality achievement and speed of solution. The DDPG improved the operation of energy storage units and was able to recognize the supply-demand operational pattern, ensuring the scalability of the VPP to cope with different energy demand levels