Study of electrical usage and demand at the container terminal

The electrical usage and demand at container terminal were studied for two years. The results provide a technique for calculating the maximum demand at container terminal with a more accurate result, leading to a substantial saving both in capital cost for electrical infrastructure investment and ongoing electricity costs.

Blood-nourishing and hard-softening capsule costs less in the management of osteoarthriticknee pain : A randomized controlled trial

The blood-nourishing and hard-softening (BNHS) capsule is a traditional Chinese formula used in the symptomatic treatment of inflammation and pain. We conducted this randomized controlled trial to compare the efficacy of BNHS with other commonly prescribed drugs. We recruited 120 patients from two teaching hospitals; 30 patients in each hospital were randomly assigned to receive BNHS. In one hospital, the 30 controls were given another traditional Chinese drug; whereas a Western medicine (chondroprotection drug/Viartril-s) was used as the control in the other hospital. Intervention was carried out over a period of 4 weeks. Primary outcome measures included self-reported pain level, and changes in stiffness and functional ability as measured by the Western Ontario McMaster Universities Osteoarthritis (WOMAC) index. Mixed models were used for statistical analysis. Substantial improvements in disease-specific symptoms were observed, after 4 weeks of treatment, in patients taking BNHS capsules. As assessed by the WOMAC index, pain level of the BNHS group decreased by 57% [95% confidence interval (CI) = 50, 63], stiffness by 63% (95% CI = 55, 71) and functional ability increased by 56% (95% CI = 50, 63). No significant differences were found in any of the outcome measures between the BNHS group and either of the comparison groups. No severe adverse effects were reported. However, this study lacked a placebo group; therefore, we conclude that BNHS appears to be as effective as commonly prescribed medicines for the relief of pain and dysfunction in knee osteoarthritis patients, but costs a lot less than other Western and herbal drugs in the study.

Prediction interval estimation for electricity price and demand using support vector machines

Uncertainty is known to be a concomitant factor of almost all the real world commodities such as oil prices, stock prices, sales and demand of products. As a consequence, forecasting problems are becoming more and more challenging and ridden with uncertainty. Such uncertainties are generally quantified by statistical tools such as prediction intervals (Pis). Pis quantify the uncertainty related to forecasts by estimating the ranges of the targeted quantities. Pis generated by traditional neural network based approaches are limited by high computational burden and impractical assumptions about the distribution of the data. A novel technique for constructing high quality Pis using support vector machines (SVMs) is being proposed in this paper. The proposed technique directly estimates the upper and lower bounds of the PI in a short time and without any assumptions about the data distribution. The SVM parameters are tuned using particle swarm optimization technique by minimization of a modified Pi-based objective function. Electricity price and demand data of the Ontario electricity market is used to validate the performance of the proposed technique. Several case studies for different months indicate the superior performance of the proposed method in terms of high quality PI generation and shorter computational times.

Prediction interval estimation of electricity prices using PSO-tuned support vector machines

Uncertainty of the electricity prices makes the task of accurate forecasting quite difficult for the electricity market participants. Prediction intervals (PIs) are statistical tools which quantify the uncertainty related to forecasts by estimating the ranges of the future electricity prices. Traditional approaches based on neural networks (NNs) generate PIs at the cost of high computational burden and doubtful assumptions about data distributions. In this work, we propose a novel technique that is not plagued with the above limitations and it generates high-quality PIs in a short time. The proposed method directly generates the lower and upper bounds of the future electricity prices using support vector machines (SVM). Optimal model parameters are obtained by the minimization of a modified PI-based objective function using a particle swarm optimization (PSO) technique. The efficiency of the proposed method is illustrated using data from Ontario, Pennsylvania-New Jersey-Maryland (PJM) interconnection day-ahead and real-time markets.

A systematic design of interval type-2 fuzzy logic system using extreme learning machine for electricity load demand forecasting

This paper presents a novel design of interval type-2 fuzzy logic systems (IT2FLS) by utilizing the theory of extreme learning machine (ELM) for electricity load demand forecasting. ELM has become a popular learning algorithm for single hidden layer feed-forward neural networks (SLFN). From the functional equivalence between the SLFN and fuzzy inference system, a hybrid of fuzzy-ELM has gained attention of the researchers. This paper extends the concept of fuzzy-ELM to an IT2FLS based on ELM (IT2FELM). In the proposed design the antecedent membership function parameters of the IT2FLS are generated randomly, whereas the consequent part parameters are determined analytically by the Moore-Penrose pseudo inverse. The ELM strategy ensures fast learning of the IT2FLS as well as optimality of the parameters. Effectiveness of the proposed design of IT2FLS is demonstrated with the application of forecasting nonlinear and chaotic data sets. Nonlinear data of electricity load from the Australian National Electricity Market for the Victoria region and from the Ontario Electricity Market are considered here. The proposed model is also applied to forecast Mackey-glass chaotic time series data. Comparative analysis of the proposed model is conducted with some traditional models such as neural networks (NN) and adaptive neuro fuzzy inference system (ANFIS). In order to verify the structure of the proposed design of IT2FLS an alternate design of IT2FLS based on Kalman filter (KF) is also utilized for the comparison purposes.