Solar pre-cooling with different tariff structures and household time of use patterns

This paper presents a clustering-based solar pre-cooling (SPC) analysis to evaluate the SPC potential of Australian housing stock. 450 households with solar PV systems and Air Conditioning (AC) are clustered into different groups based on their net electricity demand profiles excluding any AC operation. Then, the AC excluded net demand profile of each household is combined with nine different building types, creating nine virtual building envelopes for each household. Solar pre-cooling is simulated for all the virtual buildings and the results are compared with a baseline scenario in terms of maximum demand reduction, minimum demand mitigation, and cost savings, considering three different tariff structures. The results show that regardless of the energy efficiency and construction materials of a building, the SPC potential varies significantly based on the AC excluded net demand profile of the household. SPC offers high minimum demand mitigation while maximum demand reduction is not considerable. The cost savings highly depends on the tariff structure, and the Feed-in Tariff (FiT)

Numerical Simulation Modelling of Building-Integrated Photovoltaic Double-Skin Facades

Building-integrated photovoltaic (BIPV) replaces building envelope materials and provides electric power generator, which has aroused great interest for those in the fields of energy conservation and building design. Double-skin façade (DSF) has attracted significant attention over the last three decades due to its bi-layer structure, which improves thermal and acoustic insulation and therefore increases the energy efficiency and thermal comfort of buildings. It is hypothesised that the integration of BIPV and DSF (BIPV-DSF) would help buildings in reducing energy consumption and improving indoor thermal comfort concurrently. However, the prototype of the BIPV-DSF has not been well explored. Thus, the investigations of the BIPV-DSF are worthwhile. Numerical simulation is a cost and time effective measure for the design and analysis of buildings. This chapter spells out a comprehensive method of numerical simulation modelling of the novel BIPV-DSF system in buildings, which is carried out by using a graphically based design tool – TRNSYS and its plugins. TRNSYS has been validated and widely used in both the BIPV and building related research activities, which are capable in analysing the effects of BIPV-DSF on building performance such as energy consumption and indoor thermal condition

Working in partnership to develop engineering capacity in energy efficiency

Energy efficiency is a complex topic to integrate into higher education curricula, with limited success internationally or in Australia. This paper discusses one of the successful initiatives within the Energy Efficiency Training Program, which was jointly managed and implemented by the New South Wales Office of Environment and Heritage and Department of Education and Communities. The state government initiative aimed to increase the knowledge and skills of the New South Wales workforce, help business to identify and implement energy efficiency projects, and provide professional development for the training providers. Key sectors targeted included property, construction, manufacturing and services. The Program was externally evaluated over the three years 2011-2013 and a range of insights were gained through these facilitated reflective opportunities, confirming and building upon literature on the topic to date. This paper presents lessons learned from the engineering part of the program (\u27the project\u27), spanning government agencies, academic institutions, and academia. The paper begins with a contextual summary, followed by a synthesis of key learnings and implications for future training initiatives. It is intended that sharing these lessons will contribute to literature in the field, and assist other organisations in Australia and overseas planning similar initiatives

Working in partnership to develop engineering capability in energy efficiency

Energy efficiency is a complex topic to integrate into higher education curricula, with limited success internationally or in Australia. This paper discusses one of the successful initiatives within the Energy Efficiency Training Program, which was jointly managed and implemented by the New South Wales Office of Environment and Heritage and Department of Education and Communities. The state government initiative aimed to increase the knowledge and skills of the New South Wales workforce, help business to identify and implement energy efficiency projects, and provide professional development for the training providers. Key sectors targeted included property, construction, manufacturing and services. The Program was externally evaluated over the three years 2011 to 2013 and a range of insights were gained through these facilitated reflective opportunities, confirming and building upon literature on the topic to date. This paper presents lessons learned from the engineering part of the program (‘the project’), spanning government agencies, academic institutions, and academia. The paper begins with a contextual summary, followed by a synthesis of key learnings and implications for future training initiatives. It is intended that sharing these lessons will contribute to literature in the field, and assist other organisations in Australia and overseas planning similar initiatives

Optimising Design Parameters of a Building-Integrated Photovoltaic Double-Skin Facade in Different Climate Zones in Australia

Energy used in buildings is mainly attributed to provide the desired thermal comfort, which could result in an increase in carbon emission and, in turn, lead to further environmental degradation. A Building-Integrated Photovoltaic Double-Skin Façade (BIPV-DSF) is a promising way to maintain indoor thermal comfort, obtained with low environmental impact and energy consumption. The appropriate design of BIPV-DSFs can maximise indoor thermal comfort and energy efficiency for buildings. This paper presents optimal BIPV-DSF design solutions, which are dedicated to offering comfortable and energy-efficient buildings, through optimisation of the most important design parameters of a BIPV-DSF under three different climate conditions in Australia. The results illustrate how thermal transmittance (U-value) and solar heat gain coefficient (SHGC) of windows of the BIPV-DSF, as the most important design parameters, were optimised for application in the context of different climates, operation modes, and orientations. The paper contributes to the matters concerning the integrated effect of BIPV-DSFs on thermal comfort and energy performance in buildings

PV as an integrated building material: Status report, barriers and opportunities

This publication provides a status report on the project underway to identify opportunities and barriers, establish functional specifications, and propose a roadmap for the uptake of building integrated PV.Conventional photovoltaic (PV) module costs are reducing as manufacturers scale up PV production. Crystalline silicon (single or multi-crystalline wafers) still dominates the industry while thin film manufacturers, apart from CdTe, are having difficulty competing.  The cost of the PV device is decreasing most significantly as production increases.   This leaves module costs as well as Balance of Systems costs as the major barriers to further lowering costs.As costs fall, the levelised cost of electricity (LCOE) from PV is now, in many places cost competitive with retail electricity prices. PV on buildings is therefore the most economic location to install PV

Short-term forecasting of individual household electricity loads with investigating impact of data resolution and forecast horizon

Smart grid components such as smart home and battery energy management systems, high penetration of renewable energy systems, and demand response activities, require accurate electricity demand forecasts for the successful operation of the electricity distribution networks. For example, in order to optimize residential PV generation and electricity consumption and plan battery charge-discharge regimes by scheduling household appliances, forecasts need to target and be tailored to individual household electricity loads. The recent uptake of smart meters allows easier access to electricity readings at very fine resolutions; hence, it is possible to utilize this source of available data to create forecast models. In this paper, models which predominantly use smart meter data alongside with weather variables, or smart meter based models (SMBM), are implemented to forecast individual household loads. Well-known machine learning models such as artificial neural networks (ANN), support vector machines (SVM) and Least-Square SVM are implemented within the SMBM framework and their performance is compared. The analysed household stock consists of 14 households from the state of New South Wales, Australia, with at least a year worth of 5 min. resolution data. In order for the results to be comparable between different households, our study first investigates household load profiles according to their volatility and reveals the relationship between load standard deviation and forecast performance. The analysis extends previous research by evaluating forecasts over four different data resolution; 5, 15, 30 and 60 min, each resolution analysed for four different horizons; 1, 6, 12 and 24 h ahead. Both, data resolution and forecast horizon, proved to have significant impact on the forecast performance and the obtained results provide important insights for the operation of various smart grid applications. Finally, it is shown that the load profile of some households vary significantly across different days; as a result, providing a single model for the entire period may result in limited performance. By the use of a pre-clustering step, similar daily load profiles are grouped together according to their standard deviation, and instead of applying one SMBM for the entire data-set of a particular household, separate SMBMs are applied to each one of the clusters. This preliminary clustering step increases the complexity of the analysis however it results in significant improvements in forecast performance

Short-term forecasting of individual household electricity loads with investigating impact of data resolution and forecast horizon

Smart grid components such as smart home and battery energy management systems, high penetration of renewable energy systems, and demand response activities, require accurate electricity demand forecasts for the successful operation of the electricity distribution networks. For example, in order to optimize residential PV generation and electricity consumption and plan battery charge-discharge regimes by scheduling household appliances, forecasts need to target and be tailored to individual household electricity loads. The recent uptake of smart meters allows easier access to electricity readings at very fine resolutions; hence, it is possible to utilize this source of available data to create forecast models. In this paper, models which predominantly use smart meter data alongside with weather variables, or smart meter based models (SMBM), are implemented to forecast individual household loads. Well-known machine learning models such as artificial neural networks (ANN), support vector machines (SVM) and Least-Square SVM are implemented within the SMBM framework and their performance is compared. The analysed household stock consists of 14 households from the state of New South Wales, Australia, with at least a year worth of 5 min. resolution data. In order for the results to be comparable between different households, our study first investigates household load profiles according to their volatility and reveals the relationship between load standard deviation and forecast performance. The analysis extends previous research by evaluating forecasts over four different data resolution; 5, 15, 30 and 60 min, each resolution analysed for four different horizons; 1, 6, 12 and 24 h ahead. Both, data resolution and forecast horizon, proved to have significant impact on the forecast performance and the obtained results provide important insights for the operation of various smart grid applications. Finally, it is shown that the load profile of some households vary significantly across different days; as a result, providing a single model for the entire period may result in limited performance. By the use of a pre-clustering step, similar daily load profiles are grouped together according to their standard deviation, and instead of applying one SMBM for the entire data-set of a particular household, separate SMBMs are applied to each one of the clusters. This preliminary clustering step increases the complexity of the analysis however it results in significant improvements in forecast performance