Letters to editor [Letter from Brisbane]

A short memoir piece about the 2011 Brisbane floods. We’re drawing to the close of a day when, thankfully, the water level has peaked lower than forecasts had predicted. In the most extreme emergencies, homes have been picked up and washed away...

A model predictive approach for community battery energy storage system optimization

This paper presents an efficient algorithm for optimizing the operation of battery storage in a low voltage distribution network with a high penetration of PV generation. A predictive control solution is presented that uses wavelet neural networks to predict the load and PV generation at hourly intervals for twelve hours into the future. The load and generation forecast, and the previous twelve hours of load and generation history, is used to assemble load profile. A diurnal charging profile can be compactly represented by a vector of Fourier coefficients allowing a direct search optimization algorithm to be applied. The optimal profile is updated hourly allowing the state of charge profile to respond to changing forecasts in load.

Grateful fateful sunshine rain

Grateful Fateful Sunshine Rain is a permanent public artwork commissioned by Aria Property Group through a competitive process for the Austin apartment building in South Brisbane. Artist Statement: Residents of Brisbane have a complex relationship with weather. As the capital of the Sunshine State, weather is an integral part of the city’s cultural identity. Weather deeply affects the mood of the city – from the excitement of scantily clad partygoers on balmy December evenings and late February’s lethargy, to the deepening anxiety that emerges after 100 days of rain (or more commonly, 100 days without rain). With a brief nod to the city’s – now decommissioned – iconic MCL weather beacon, Grateful Fateful Sunshine Rain taps into this aspect of Brisbane’s psyche with poetic, illuminated visualisations of real-time weather forecasts issued by the Bureau of Meteorology. Each evening, the artwork downloads tomorrow’s forecast from the Bureau of Meteorology website. Data including, current local temperature, humidity, wind speed & direction, precipitation (rain, hail etc), are used to generate a lighting display that conveys how tomorrow will feel. The artwork’s background colour indicates the expected temperature – from cold blues through mild pastel pinks and blues to bright hot oranges and reds. White fluffy clouds roll across the artwork if cloud is predicted. The density of these clouds indicates the level of cover whilst movement indicates expected wind speed and direction. If rain is predicted, sparkles of white light will appear on top of whichever background colour is chosen for the next day’s temperature. Sparkles appear constantly before wet, drizzly days, and intermittently if scattered showers are predicted. Intermittent, but more intense sparkles appear before rain storms or thunderstorms. Research Contribution: The work has made contributions to the field in the way it rethinks approaches to the conceptualization, design and realization of illuminated urban media. This has led to new theorizations of urban media, which consider light and illumination can be used to convey meaningful data. The research has produced new methods for controlling illumination systems using tools and techniques typically employed in computation arts. It has also develop methods and processes for the design and production of illuminated urban media architectures that are connected to real time data sources, and do which not follow the assumed logics of screen based media and displays.

Are we there yet? Australian road safety targets and road traffic crash fatalities

Background Road safety targets are widely used and provide a basis for evaluating progress in road safety outcomes against a quantified goal. In Australia, a reduction in fatalities from road traffic crashes (RTCs) is a public policy objective: a national target of no more than 5.6 fatalities per 100,000 population by 2010 was set in 2001. The purpose of this paper is to examine the progress Australia and its states and territories have made in reducing RTC fatalities, and to estimate when the 2010 target may be reached by the jurisdictions. Methods Following a descriptive analysis, univariate time-series models estimate past trends in fatality rates over recent decades. Data for differing time periods are analysed and different trend specifications estimated. Preferred models were selected on the basis of statistical criteria and the period covered by the data. The results of preferred regressions are used to determine out-of-sample forecasts of when the national target may be attained by the jurisdictions. Though there are limitations with the time series approach used, inadequate data precluded the estimation of a full causal/structural model. Results Statistically significant reductions in fatality rates since 1971 were found for all jurisdictions with the national rate decreasing on average, 3% per year since 1992. However the gains have varied across time and space, with percent changes in fatality rates ranging from an 8% increase in New South Wales 1972-1981 to a 46% decrease in Queensland 1982-1991. Based on an estimate of past trends, it is possible that the target set for 2010 may not be reached nationally, until 2016. Unsurprisingly, the analysis indicated a range of outcomes for the respective state/territory jurisdictions though these results should be interpreted with caution due to different assumptions and length of data. Conclusions Results indicate that while Australia has been successful over recent decades in reducing RTC mortality, an important gap between aspirations and achievements remains. Moreover, unless there are fairly radical ("trend-breaking") changes in the factors that affect the incidence of RTC fatalities, deaths from RTCs are likely to remain above the national target in some areas of Australia, for years to come.

On the benefits of equicorrelation for portfolio allocation

The importance of modelling correlation has long been recognised in the field of portfolio management, with largedimensional multivariate problems increasingly becoming the focus of research. This paper provides a straightforward and commonsense approach toward investigating a number of models used to generate forecasts of the correlation matrix for large-dimensional problems.We find evidence in favour of assuming equicorrelation across various portfolio sizes, particularly during times of crisis. During periods of market calm, however, the suitability of the constant conditional correlation model cannot be discounted, especially for large portfolios. A portfolio allocation problem is used to compare forecasting methods. The global minimum variance portfolio and Model Confidence Set are used to compare methods, while portfolio weight stability and relative economic value are also considered.

Connections not boundaries: Future learning and physical spaces

In the developed world, we feel the effects of "digital disruption" in our experiences of the spaces of retail, hospitality, entertainment, finance, arts and culture, and even healthcare. This disruption can take many forms: augmentation of physical experience with a digital complement such as the use of a bespoke mobile application to navigate an art museum, ordering food on digital tablets in a restaurant, recording our health data to share with a doctor. We also rate and review our experiences of a wide range of services and share these opinions with diverse others via the social web.

Anticipating Chinese Tourists Arrivals in Australia: A Time Series Analysis

Given the growing importance of the Chinese tourist market to Australia, an understanding of Chinese tourists' arrival patterns is essential to accurate forecasting of future arrivals. Drawing on 25 years of records (1991-2015), this study developed a time-series model of monthly arrivals of Chinese tourists in Australia. The model reflects the exponentially increasing trend and strong seasonality of arrivals. Excellent results from validation of the model's forecasts endorsed this time-series model's potential in the policy prescription and management practice of Australian tourism industries.

A flexible tool for integrated planning of active distribution networks

This paper presents a flexible and integrated planning tool for active distribution network to maximise the benefits of having high level s of renewables, customer engagement, and new technology implementations. The tool has two main processing parts: “optimisation” and “forecast”. The “optimization” part is an automated and integrated planning framework to optimize the net present value (NPV) of investment strategy for electric distribution network augmentation over large areas and long planning horizons (e.g. 5 to 20 years) based on a modified particle swarm optimization (MPSO). The “forecast” is a flexible agent-based framework to produce load duration curves (LDCs) of load forecasts for different levels of customer engagement, energy storage controls, and electric vehicles (EVs). In addition, “forecast” connects the existing databases of utility to the proposed tool as well as outputs the load profiles and network plan in Google Earth. This integrated tool enables different divisions within a utility to analyze their programs and options in a single platform using comprehensive information.

Forecasting day-ahead electricity load using a multiple equation time series approach

The quality of short-term electricity load forecasting is crucial to the operation and trading activities of market participants in an electricity market. In this paper, it is shown that a multiple equation time-series model, which is estimated by repeated application of ordinary least squares, has the potential to match or even outperform more complex nonlinear and nonparametric forecasting models. The key ingredient of the success of this simple model is the effective use of lagged information by allowing for interaction between seasonal patterns and intra-day dependencies. Although the model is built using data for the Queensland region of Australia, the method is completely generic and applicable to any load forecasting problem. The model’s forecasting ability is assessed by means of the mean absolute percentage error (MAPE). For day-ahead forecast, the MAPE returned by the model over a period of 11 years is an impressive 1.36%. The forecast accuracy of the model is compared with a number of benchmarks including three popular alternatives and one industrial standard reported by the Australia Energy Market Operator (AEMO). The performance of the model developed in this paper is superior to all benchmarks and outperforms the AEMO forecasts by about a third in terms of the MAPE criterion.