PNG Coffee Industry Board Office and Warehouse, Goroka

View past tiered amphitheatre and offices above to air strip beyond.

View along western elevation, PNG Coffee Industry Board Office and Warehouse, Goroka

View past timber blinds to balcony and timber sunscreens.

Under construction, PNG Coffee Industry Board Office and Warehouse, Goroka

View of post being hoisted into position during construction.

Warehouse, PNG Coffee Industry Board Office and Warehouse, Goroka

View of warehouse exterior.

Pole detail, PNG Coffee Industry Board Office and Warehouse, Goroka

Detailed view of poles used in construction. Poles were spliced in their length with steel bars (like 3 pin plugs) and these joints were restrained from splitting with steel strap belts. The belts were tightened with opposing wedges like the old Greene & Greene wrought iron detail.

Warehouse, PNG Coffee Industry Board Office and Warehouse, Goroka

View of warehouse exterior.

Roof structure, internal, PNG Coffee Industry Board Office and Warehouse, Goroka

View to underside of roof with steel beam and insulation.

Post and beam connection, PNG Coffee Industry Board Office and Warehouse, Goroka

Detailed view of cast iron brackets connecting beams and posts.

Front elevation, PNG Coffee Industry Board Office and Warehouse, Goroka

View of front elevation with entrance from exterior.

Post and beam connection, PNG Coffee Industry Board Office and Warehouse, Goroka

Detailed view of cast iron brackets connecting beams and posts.

Special issue on advances in data mining and its applications

Data mining is the process to identify valid, implicit, previously unknown, potentially useful and understandable information from large databases. It is an important step in the process of knowledge discovery in databases, (Olaru & Wehenkel, 1999). In a data mining process, input data can be structured, seme-structured, or unstructured. Data can be in text, categorical or numerical values. One of the important characteristics of data mining is its ability to deal data with large volume, distributed, time variant, noisy, and high dimensionality. A large number of data mining algorithms have been developed for different applications. For example, association rules mining can be useful for market basket problems, clustering algorithms can be used to discover trends in unsupervised learning problems, classification algorithms can be applied in decision-making problems, and sequential and time series mining algorithms can be used in predicting events, fault detection, and other supervised learning problems (Vapnik, 1999). Classification is among the most important tasks in the data mining, particularly for data mining applications into engineering fields. Together with regression, classification is mainly for predictive modelling. So far, there have been a number of classification algorithms in practice. According to (Sebastiani, 2002), the main classification algorithms can be categorized as: decision tree and rule based approach such as C4.5 (Quinlan, 1996); probability methods such as Bayesian classifier (Lewis, 1998); on-line methods such as Winnow (Littlestone, 1988) and CVFDT (Hulten 2001), neural networks methods (Rumelhart, Hinton & Wiliams, 1986); example-based methods such as k-nearest neighbors (Duda & Hart, 1973), and SVM (Cortes & Vapnik, 1995). Other important techniques for classification tasks include Associative Classification (Liu et al, 1998) and Ensemble Classification (Tumer, 1996).

A framework for electricity price spike analysis with advanced data mining methods

There are many techniques for electricity market price forecasting. However, most of them are designed for expected price analysis rather than price spike forecasting. An effective method of predicting the occurrence of spikes has not yet been observed in the literature so far. In this paper, a data mining based approach is presented to give a reliable forecast of the occurrence of price spikes. Combined with the spike value prediction techniques developed by the same authors, the proposed approach aims at providing a comprehensive tool for price spike forecasting. In this paper, feature selection techniques are firstly described to identify the attributes relevant to the occurrence of spikes. A simple introduction to the classification techniques is given for completeness. Two algorithms: support vector machine and probability classifier are chosen to be the spike occurrence predictors and are discussed in details. Realistic market data are used to test the proposed model with promising results.