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Energy forecasting plays a vital role in mitigating challenges in data rich smart grid (SG) systems involving various applications such as demand‐side management, load shedding, and optimum dispatch. Managing efficient forecasting while ensuring the least possible prediction error is one of the main challenges posed in the grid today, considering the uncertainty in SG data. This paper presents a comprehensive and application‐oriented review of state‐of‐the‐art forecasting methods for SG systems along with recent developments in probabilistic deep learning (PDL). Traditional point forecasting methods including statistical, machine learning (ML), and deep learning (DL) are extensively investigated in terms of their applicability to energy forecasting. In addition, the significance of hybrid and data pre‐processing techniques to support forecasting performance is also studied. A comparative case study using the Victorian electricity consumption in Australia and American electric power (AEP) datasets is conducted to analyze the performance of deterministic and probabilistic forecasting methods. The analysis demonstrates higher efficacy of DL methods with appropriate hyper‐parameter tuning when sample sizes are larger and involve nonlinear patterns. Furthermore, PDL methods are found to achieve at least 60% lower prediction errors in comparison to other benchmark DL methods. However, the execution time increases significantly for PDL methods due to large sample space and a tradeoff between computational performance and forecasting accuracy needs to be maintained. [ABSTRACT FROM AUTHOR]

Australia's electricity networks are experiencing low demand during the day due to excessive residential solar export and high demand during the evening on days of extreme temperature due to high air conditioning use. Pre-cooling and solar pre-cooling are demand-side management strategies with the potential to address both these issues. However, there remains a lack of comprehensive studies into the potential of pre-cooling and solar pre-cooling due to a lack of data. In Australia, however, extensive datasets of household energy measurements, including consumption and generation from rooftop solar, obtained through retailer-owned smart meters and household-owned third-party monitoring devices, are now becoming available. However, models presented in the literature which could be used to simulate the cooling energy in residential homes are temperature-based, requiring indoor temperature as an input. Temperature-based models are, therefore, precluded from being able to use these newly available and extensive energy-based datasets, and there is a need for the development of new energy-based simulation tools. To address this gap, a novel data-driven model to estimate the cooling energy in residential homes is proposed. The model is temperature-independent, requiring only energy-based datasets for input. The proposed model was derived by an analysis comparing the internal free-running and air conditioned temperature data and the air conditioning data for template residential homes generated by AccuRate, a building energy simulation tool. The model is comprised of four linear equations, where their respective slope intercepts represent a thermal efficiency metric of a thermal zone in the template residential home. The model can be used to estimate the difference between the internal free-running, and air conditioned temperature, which is equivalent to the cooling energy in the thermal zone. Error testing of the model compared the difference between the estimated and AccuRate air conditioned temperature and gave average CV-RMSE and MAE values of 22% and 0.3 °C, respectively. The significance of the model is that the slope intercepts for a template home can be applied to an actual residential home with equivalent thermal efficiency, and a pre-cooling or solar pre-cooling analysis is undertaken using the model in combination with the home's energy-based dataset. The model is, therefore, able to utilise the newly available extensive energy-based datasets for comprehensive studies on pre-cooling and solar pre-cooling of residential homes. [ABSTRACT FROM AUTHOR]

Abstract: This paper summarises developments in the promotion and implementation of cleaner production (CP) and eco-efficiency (EE) in Western Australia (WA), in four stages: groundwork (1996–1999), experimentation (1999–2002), roll out (2002–2004) and reorientation (2004 onward). The remoteness of WA and the dominance of the minerals, energy and agribusiness industries, contributed to the late interest in CP and relatively slow start. Pioneers in government, industry and academia started to come together in 1998–1999. Subsequent clarification of concepts and design of programs resulted in a two-pronged strategy to create both a supply and demand for CP services. This enabled a rapid increase in interest in CP, which started to level in 2004. Although this is partially due to external circumstances, it also appears that the limits of current CP and EE theory and practice have been reached. These limits appear to be at least two fold. First, current policies and program designs appear not yet able to achieve a step-increase in the number of businesses involved in CP. Insights from innovation and social marketing theory and practice are now being incorporated in the design and delivery of the next generation of CP programs. Second, it appears that mainstream CP tools are insufficiently catered to the technological and organisational complexity of many industries. Greater engineering and management depth in CP tools might be required to mainstream CP in routine process design, continuous improvement and change management practices. [Copyright &y& Elsevier]

Australian urban water utilities face a significant challenge in designing appropriate demand management and supply augmentation policies in the presence of significant water scarcity and climate variability. This article considers the design of optimal demand management and supply augmentation policies for urban water. In particular, scarcity pricing is considered as a potential alternative to the predominant demand management policy of water restrictions. A stochastic dynamic programming model of an urban water market is developed based on data from the ACT region. Given a specification of the demand and supply for urban water state dependent optimal price and investment policies are estimated. The results illustrate how the optimal urban water price varies inversely with the prevailing storage level and how the optimal timing of investment differs significantly between rain dependent and rain independent augmentation options. [ABSTRACT FROM AUTHOR]