Adam Clements

Mobius-Like Mappings and Their Use in Kernel Density Estimation

It is well known that the manipulation of sample data by means of a parametric function can improve the performance of kernel density estimation. This article proposes a two-parameter Mobius-like function to map sample data drawn from a semi-infinite space into [−1,1). A standard kernel method is then used to estimate the density. The proposed method is shown to yield effective estimates of density and is computationally more efficient than other well-known transformation methods. The efficacy of the technique is demonstrated in a practical setting by application to two datasets.

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.

Semi-parametric Forecasting of Spikes in Electricity Prices

The occurrence of extreme movements in the spot price of electricity represents a significant source of risk to retailers. A range of approaches have been considered with respect to modelling electricity prices; these models, however, have relied on time-series approaches, which typically use restrictive decay schemes placing greater weight on more recent observations. This study develops an alternative, semi-parametric method for forecasting, which uses state-dependent weights derived from a kernel function. The forecasts that are obtained using this method are accurate and therefore potentially useful to electricity retailers in terms of risk management.

Semi-Parametric Forecasting of Realized Volatility

Forecasts generated by time series models traditionally place greater weight on more recent observations. This paper develops an alternative semi-parametric method for forecasting that does not rely on this convention and applies it to the problem of forecasting asset return volatility. In this approach, a forecast is a weighted average of historical volatility, with the greatest weight given to periods that exhibit similar market conditions to the time at which the forecast is being formed. Weighting is determined by comparing short-term trends in volatility across time (as a measure of market conditions) by means of a multivariate kernel scheme. It is found that the semi-parametric method produces forecasts that are significantly more accurate than a number of competing approaches at both short and long forecast horizons.

Modeling electricity price events as point processes

Energy prices are highly volatile and often feature unexpected spikes. It is the aim of this paper to examine whether the occurrence of these extreme price events displays any regularities that can be captured using an econometric model. Here we treat these price events as point processes and apply Hawkes and Poisson autoregressive models to model the dynamics in the intensity of this process.We use load and meteorological information to model the time variation in the intensity of the process. The models are applied to data from the Australian wholesale electricity market, and a forecasting exercise illustrates both the usefulness of these models and their limitations when attempting to forecast the occurrence of extreme price events.

The Australian electricity market’s pre-dispatch process: Some observations on its efficiency using ordered probit model

Many market participants in Australia Electricity Market had cast doubts on whether the pre-dispatch process in the electricity market is able to give them good and timely quantity and price information. In a study by [11], they observed a significant bias (mainly indicating that the pre-dispatch process tends to underestimate spot price outcomes), a seasonality features of the bias across seasons and/or trading periods and changes in bias across the years in our sample period (1999 to 2007). In a formal setting of an ordered probit model we establish that there are some exogenous variables that are able to explain increased probabilities of over- or under-predictions of the spot price. It transpires that meteorological data, expected pre-dispatch prices and information on past over- and under-predictions contribute significantly to explaining variation in the probabilities for over- and under-predictions. The results allow us to conjecture that some of the bids and re-bids provided by electricity generators are not made in good faith.