Prediction of monthly rainfall in Victoria, Australia : Clusterwise linear regression approach
- Authors: Bagirov, Adil , Mahmood, Arshad , Barton, Andrew
- Date: 2017
- Type: Text , Journal article
- Relation: Atmospheric Research Vol. 188, no. (2017), p. 20-29
- Relation: http://purl.org/au-research/grants/arc/DP140103213
- Full Text: false
- Reviewed:
- Description: This paper develops the Clusterwise Linear Regression (CLR) technique for prediction of monthly rainfall. The CLR is a combination of clustering and regression techniques. It is formulated as an optimization problem and an incremental algorithm is designed to solve it. The algorithm is applied to predict monthly rainfall in Victoria, Australia using rainfall data with five input meteorological variables over the period of 1889–2014 from eight geographically diverse weather stations. The prediction performance of the CLR method is evaluated by comparing observed and predicted rainfall values using four measures of forecast accuracy. The proposed method is also compared with the CLR using the maximum likelihood framework by the expectation-maximization algorithm, multiple linear regression, artificial neural networks and the support vector machines for regression models using computational results. The results demonstrate that the proposed algorithm outperforms other methods in most locations. © 2017 Elsevier B.V.
A comparative assessment of models to predict monthly rainfall in Australia
- Authors: Bagirov, Adil , Mahmood, Arshad
- Date: 2018
- Type: Text , Journal article
- Relation: Water Resources Management Vol. 32, no. 5 (2018), p. 1777-1794
- Relation: http://purl.org/au-research/grants/arc/DP140103213
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- Description: Accurate rainfall prediction is a challenging task. It is especially challenging in Australia where the climate is highly variable. Australia’s climatic zones range from high rainfall tropical regions in the north to the driest desert region in the interior. The performance of prediction models may vary depending on climatic conditions. It is, therefore, important to assess and compare the performance of these models in different climatic zones. This paper examines the performance of data driven models such as the support vector machines for regression, the multiple linear regression, the k-nearest neighbors and the artificial neural networks for monthly rainfall prediction in Australia depending on climatic conditions. Rainfall data with five meteorological variables over the period of 1970–2014 from 24 geographically diverse weather stations are used for this purpose. The prediction performance of each model was evaluated by comparing observed and predicted rainfall using various measures for prediction accuracy. © 2018, Springer Science+Business Media B.V., part of Springer Nature.
Temporal model of the drivers of household PV purchase in Australia
- Authors: Currie, Glen , Evans, Robin , Duffield, Colin , Mareels, Iven
- Date: 2019
- Type: Text , Journal article
- Relation: Technology and economics of smart grids and sustainable energy Vol. 4, no. 1 (2019), p. 1-7
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- Description: Australian household PV adoption rates are the highest in the world and this is causing a rise in technical problems and the cost of the distribution system. This paper offers a predictive model of household PV purchases in Australia and this could be used in policy to better manage PV uptake patterns. The analysis used 1.6 million domestic PV installation decisions over 11 years from 2006 to 2017 and is statistically significant. Autoregressive integrated moving average (ARIMA) modelling was used to reduce non-stationarity in the data and Granger Causal modelling showed the most effective policy levers are price, subsidy, business confidence and PV feed-in tariffs. This analysis develops a model of Australian PV adoption and increases understanding of consumer roles in the future electricity system. This is compared to other similar models in the literature. The key contribution is that the scale of the model creates a temporal prediction that is not in other literature. The second contribution is that the model may apply to other household energy decisions. This was measured by comparing Australian PV adoption to solar hot water adoption.