The novel as social satire: 60 years later, the wind done gone and the limitations of fair use

The absence of the doctrine of fair use from Australian copyright law has been a bone of contention in Australia after the Australia-United States Free Trade Agreement (FTA). As the Australian government reformed the Copyright Act 1968 (Cth) in the aftermath of the FTA it eschewed the option of adopting fair use. Instead, Australia chose to incorporate a version of fair use into its existing fair dealing framework. Accordingly, the Copyright Amendment Act 2006 (Cth) inserted ss 41A and 103AA into the Copyright Act. These provisions provide that a fair dealing with a copyright protected work does not constitute an infringement if it is done for the purposes of parody or satire. These provisions codify part of the ratio of the United States Supreme Court in the seminal case of Campbell v Acuff Rose Music. However, the parameters of these new provisions are unexplored and the sparse nature of fair dealing jurisprudence means that the true meaning of the provisions is unclear. Moreover, two cases from the United States, SunTrust Bank v Houghton Mifflin and Salinger v Colting, underline just how important it is to have legal rules that protect literary ‘re-writes’. Both cases involved authors using an original novel to ‘write back’ to the original author and the broader culture. ‘Writing back’ or the ‘re-write’ has a firm basis in literature. It adds something invaluable to our culture. The key question is whether our legal landscape can allow it to flourish. This paper examines the interaction between fair use and literary re-writes.

Prediction intervals for short-term wind farm power generation forecasts

Quantification of uncertainties associated with wind power generation forecasts is essential for optimal management of wind farms and their successful integration into power systems. This paper investigates two neural network-based methods for direct and rapid construction of prediction intervals (PIs) for short-term forecasting of power generation in wind farms. The lower upper bound estimation and bootstrap methods are used to quantify uncertainties associated with forecasts. The effectiveness and efficiency of these two general methods for uncertainty quantification is examined using twenty four month data from a wind farm in Australia. PIs with a confidence level of 90% are constructed for four forecasting horizons: five, ten, fifteen, and thirty minutes. Quantitative measures are applied for objective evaluation and unbiased comparison of PI quality. Demonstrated results indicate that reliable PIs can be constructed in a short time without resorting to complicate computational methods or models. Also quantitative comparison reveals that bootstrap PIs are more suitable for short prediction horizon, and lower upper bound estimation PIs are more appropriate for longer forecasting horizons.

An approach for wind power integration using demand side resources

Renewable energy resources, especially wind power, are expected to provide a considerable portion of the world energy requirements in the near future. Large-scale wind power penetration impacts the electricity industry in many aspects and raises a number of technical challenges for the electricity network. A day-ahead network-constrained market clearing formulation is proposed which considers demand side resources. The proposed approach can provide flexible load profile and reduce the need for ramp up/down services by the conventional generators. This method can potentially facilitate a large penetration of wind power by shifting the wind power generation from the off-peak periods to the high-peak hours. The validity of the proposed approach has been verified using the IEEE 30 bus and 57 bus test systems.

Evaluation and comparison of type reduction algorithms from a forecast accuracy perspective

A variety of type reduction (TR) algorithms have been proposed for interval type-2 fuzzy logic systems (IT2 FLSs). The focus of existing literature is mainly on computational requirements of TR algorithm. Often researchers give more rewards to computationally less expensive TR algorithms. This paper evaluates and compares five frequently used TR algorithms from a forecasting performance perspective. Algorithms are judged based on the generalization power of IT2 FLS models developed using them. Four synthetic and real world case studies with different levels of uncertainty are considered to examine effects of TR algorithms on forecasts accuracies. It is found that Coupland-Jonh TR algorithm leads to models with a better forecasting performance. However, there is no clear relationship between the width of the type reduced set and TR algorithm.

Influences of wind energy integration into the distribution network

Wind energy is one of the most promising renewable energy sources due to its availability and climate-friendly attributes. Large-scale integration of wind energy sources creates potential technical challenges due to the intermittent nature that needs to be investigated and mitigated as part of developing a sustainable power system for the future. Therefore, this study developed simulation models to investigate the potential challenges, in particular voltage fluctuations, zone substation, and distribution transformer loading, power flow characteristics, and harmonic emissions with the integration of wind energy into both the high voltage (HV) and low voltage (LV) distribution network (DN). From model analysis, it has been clearly indicated that influences of these problems increase with the increased integration of wind energy into both the high voltage and low voltage distribution network, however, the level of adverse impacts is higher in the LV DN compared to the HV DN.

Short-term load and wind power forecasting using neural network-based prediction intervals

Electrical power systems are evolving from today's centralized bulk systems to more decentralized systems. Penetrations of renewable energies, such as wind and solar power, significantly increase the level of uncertainty in power systems. Accurate load forecasting becomes more complex, yet more important for management of power systems. Traditional methods for generating point forecasts of load demands cannot properly handle uncertainties in system operations. To quantify potential uncertainties associated with forecasts, this paper implements a neural network (NN)-based method for the construction of prediction intervals (PIs). A newly introduced method, called lower upper bound estimation (LUBE), is applied and extended to develop PIs using NN models. A new problem formulation is proposed, which translates the primary multiobjective problem into a constrained single-objective problem. Compared with the cost function, this new formulation is closer to the primary problem and has fewer parameters. Particle swarm optimization (PSO) integrated with the mutation operator is used to solve the problem. Electrical demands from Singapore and New South Wales (Australia), as well as wind power generation from Capital Wind Farm, are used to validate the PSO-based LUBE method. Comparative results show that the proposed method can construct higher quality PIs for load and wind power generation forecasts in a short time.