Combining the wavelet transform and forecasting models to predict gas forward prices

This paper presents a forecasting technique for forward energy prices, one day ahead. This technique combines a wavelet transform and forecasting models such as multi- layer perceptron, linear regression or GARCH. These techniques are applied to real data from the UK gas markets to evaluate their performance. The results show that the forecasting accuracy is improved significantly by using the wavelet transform. The methodology can be also applied to forecasting market clearing prices and electricity/gas loads.

Risk-based maintenance model for offshore oil and gas pipelines:a case study

Offshore oil and gas pipelines are vulnerable to environment as any leak and burst in pipelines cause oil/gas spill resulting in huge negative Impacts on marine lives. Breakdown maintenance of these pipelines is also cost-intensive and time-consuming resulting in huge tangible and intangible loss to the pipeline operators. Pipelines health monitoring and integrity analysis have been researched a lot for successful pipeline operations and risk-based maintenance model is one of the outcomes of those researches. This study develops a risk-based maintenance model using a combined multiple-criteria decision-making and weight method for offshore oil and gas pipelines in Thailand with the active participation of experienced executives. The model's effectiveness has been demonstrated through real life application on oil and gas pipelines in the Gulf of Thailand. Practical implications. Risk-based inspection and maintenance methodology is particularly important for oil pipelines system, as any failure in the system will not only affect productivity negatively but also has tremendous negative environmental impact. The proposed model helps the pipelines operators to analyze the health of pipelines dynamically, to select specific inspection and maintenance method for specific section in line with its probability and severity of failure.

Short-term electricity demand and gas price forecasts using wavelet transforms and adaptive models

This paper presents some forecasting techniques for energy demand and price prediction, one day ahead. These techniques combine wavelet transform (WT) with fixed and adaptive machine learning/time series models (multi-layer perceptron (MLP), radial basis functions, linear regression, or GARCH). To create an adaptive model, we use an extended Kalman filter or particle filter to update the parameters continuously on the test set. The adaptive GARCH model is a new contribution, broadening the applicability of GARCH methods. We empirically compared two approaches of combining the WT with prediction models: multicomponent forecasts and direct forecasts. These techniques are applied to large sets of real data (both stationary and non-stationary) from the UK energy markets, so as to provide comparative results that are statistically stronger than those previously reported. The results showed that the forecasting accuracy is significantly improved by using the WT and adaptive models. The best models on the electricity demand/gas price forecast are the adaptive MLP/GARCH with the multicomponent forecast; their MSEs are 0.02314 and 0.15384 respectively.

Consensus on recording of gas permeable contact lens fit

Purpose: To develop a new schematic scheme for efficiently recording the key parameters of gas permeable contact lens (GP) fits based on current consensus. Methods: Over 100 established GP fitters and educators met to discuss the parameters proposed in educational material for evaluating GP fit and concluded on the key parameters that should be recorded. The accuracy and variability of evaluating the fluorescein pattern of GP fit was determined by having 35 experienced contact lens practitioners from across the world, grading 5 images of a range of fits and the topographer simulation of the same fits, in random, order using the proposed scheme. The accuracy of the grading was compared to objective image analysis of the fluorescein intensity of the same images. Results: The key information to record to adequately describe the fit of an GP was agreed as: the manufacturer, brand and lens parameters; settling time; comfort on a 5 point scale; centration; movement on blink on a ±2 scale; and the Primary Fluorescein Pattern in the central, mid-peripheral and edge regions of the lens averaged along the horizontal and vertical lens axes, on a ±2 scale. On average 50-60% of practitioners selected the median grade when subjectively rating fluorescein intensity and this was correlated to objective quantification (r= 0.602, p< 0.001). Objective grading suggesting horizontal median fluorescein intensity was generally symmetrical, as was the vertical meridian, but this was not the case for subjective grading. Simulated fluorescein patterns were subjectively and objectively graded as being less intense than real photographs (p< 0.01). Conclusion: GP fit recording can be standardised and simplified to enhance GP practice. © 2013 British Contact Lens Association.