A novel hybrid technique for short-term electricity price forecasting in deregulated electricity markets

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Short-term electricity price forecasting is now crucial practice in deregulated electricity markets, as it forms the basis for maximizing the profits of the market participants. In this thesis, short-term electricity prices are forecast using three different predictor schemes, Artificial Neural Networks (ANNs), Support Vector Machine (SVM) and a hybrid scheme, respectively. ANNs are the very popular and successful tools for practical forecasting. In this thesis, a hidden-layered feed-forward neural network with back-propagation has been adopted for detailed comparison with other forecasting models. SVM is a newly developed technique that has many attractive features and good performance in terms of prediction. In order to overcome the limitations of individual forecasting models, a hybrid technique that combines Fuzzy-C-Means (FCM) clustering and SVM regression algorithms is proposed to forecast the half-hour electricity prices in the UK electricity markets. According to the value of their power prices, thousands of the training data are classified by the unsupervised learning method of FCM clustering. SVM regression model is then applied to each cluster by taking advantage of the aggregated data information, which reduces the noise for each training program. In order to demonstrate the predictive capability of the proposed model, ANNs and SVM models are presented and compared with the hybrid technique based on the same training and testing data sets in the case studies by using real electricity market data. The data was obtained upon request from APX Power UK for the year 2007. Mean Absolute Percentage Error (MAPE) is used to analyze the forecasting errors of different models and the results presented clearly show that the proposed hybrid technique considerably improves the electricity price forecasting

Well-posedness of grating diffraction problems for plane wave incidence: explicit dependence on wavenumbers and incident angles

Suppose that a plane wave is incident onto an impenetrable grating profile of Dirichlet or Impedance type or a penetrable grating. The grating interface is assumed to be given by a Lipschitz function in two dimensions. We derive stability estimate of the grating diffraction problem via variational method with an explicit dependence of solutions on the incident wavenumber and incident angle

Finite element and integral equation methods to conical diffraction by imperfectly conducting gratings

In this paper we study the variational method and integral equation methods for a conical diffraction problem for imperfectly conducting gratings modeled by the impedance boundary value problem of the Helmholtz equation in periodic structures. We justify the strong ellipticity of the sesquilinear form corresponding to the variational formulation and prove the uniqueness of solutions at any frequency. Convergence of the finite element method using the transparent boundary condition (Dirichlet-to-Neumann mapping) is verified. The boundary integral equation method is also discussed

The Contribution of Population Health and Demographic Change to Economic Growth in China and India

We find that a cross-country model of economic growth successfully tracks the growth takeoffs in China and India. The major drivers of the predicted takeoffs are improved health, increased openness to trade, and a rising labor force-to-population ratio due to fertility decline. We also explore the effect of the reallocation of labor from low-productivity agriculture to the higher productivity industry and service sectors. Including the money value of longevity improvements in a measure of full income reduces the gap between the magnitude of China's takeoff relative to India's due to the relative stagnation in life expectancy in China since 1980.aging, health, retirement

Hypaconitine confers protection on ketamine-induced neuronal injury in neonatal rat brain via a mechanism involving PI3K/Akt/Bcl-2 pathway

Purpose: To investigate the neuroprotective effect of hypaconitine against ketamine-induced neuronal injury in the brains of neonatal rats, and the underlying mechanism of action. Methods: Seven day-old Sprague-Dawley pups weighing 15.0 to 20.0 g (mean weight = 17.5 ± 2.5 g), and aged 7 days were used for this study. The pups were sacrificed, and their forebrains isolated and used to prepare cell suspensions. The isolated cells were treated with ketamine (100 µM) or varied concentrations of hypaconitine (0.1 – 2 µM) or LY294002 (10 µM). The cells were trypsinized and cultured at 37 °C in 10 % fetal bovine serum (FBS) supplemented Dulbecco's modified Eagle's medium (DMEM) in a humidified incubator containing 5 % CO2. Cell viability was determined using MTT assay, while TUNEL assay was used to determine the extent of apoptosis in the cells. The expressions of pAkt, Bcl-2 and caspase-3 were determined using Western blotting. Results: There were only few viable cells in the ketamine-treated group, and cell viability was significantly and dose-dependently increased in hypaconitine-treated groups (p < 0.05). The extent of apoptosis was significantly higher in ketamine-treated cells than in control cells, but treatment with hypaconitine significantly reduced the number of apoptotic cells (p < 0.05). However, in the presence of LY294002 (a PI3K-specific inhibitor), the effect of hypaconitine on neuronal cell apoptosis was significantly reversed (p < 0.05). The expressions of p-Akt and Bcl-2 were significantly down-regulated while the expression of caspase-3 was significantly upregulated in ketamine-treated neuronal cells, when compared with control group (p < 0.05). However, in cells treated with hypaconitine, the expressions of p-Akt and Bcl-2 were significantly upregulated, while the expression of caspase-3 was significantly down-regulated (p < 0.05). Treatment of neuronal cells with hypaconitine in the presence of LY294002 significantly reversed the effect of hypaconitine on the expressions of p-Akt, Bcl-2 and caspase-3 (p < 0.05). Conclusion: These results suggest that hypaconitine ameliorates ketamine-induced neuronal injury in neonatal rats via a mechanism involving the PI3K/Akt/Bcl-2 pathway

Securitization and Bank Credit Risk: Empirical Evidence from the US Commercial Banks

The main objective of this dissertation is to empirically analysis the effect of securitization activities on bank risks, and the influence of other bank specific factors are also considered simultaneously. Additionally, this analysis further examines the relationship between securitization activities and long-term bank stability. The sample data selected to make analysis is the balanced data of 149 US commercial banks from 2007 to 2017, which could represent both 2007-2009 Financial Crisis period and post-crisis period. In particular, we employ panel data approach to make correlation estimation by pooled OLS method, fixed effect model and random model. From test results, we find that securitization has a significant positive effect on bank credit risk level while negatively influence bank long-term stability. That is, the more securitized assets owned by banks, the bank would be riskier and more erratic

Enzymatic Synthesis of Functional Structured Lipids from Glycerol and Naturally Phenolic Antioxidants

Glycerol is a valuable by-product in biodiesel production by transesterification, hydrolysis reaction, and soap manufacturing by saponification. The conversion of glycerol into value-added products has attracted growing interest due to the dramatic growth of the biodiesel industry in recent years. Especially, phenolic structured lipids have been widely studied due to their influence on food quality, which have antioxidant properties for the lipid food preservation. Actually, they are triacylglycerols that have been modified with phenolic acids to change their positional distribution in glycerol backbone by enzymatically catalyzed reactions. Due to lipases’ fatty acid selectivity and regiospecificity, lipase-catalyzed reactions have been promoted for offering the advantage of greater control over the positional distribution of fatty acids in glycerol backbone. Moreover, microreactors were applied in a wide range of enzymatic applications. Nowadays, phenolic structured lipids have attracted attention for their applications in cosmetic, pharmaceutical, and food industries, which definitely provide attributes that consumers will find valuable. Therefore, it is important that further research be conducted that will allow for better understanding and more control over the various esterification/transesterification processes and reduction in costs associated with large-scale production of the bioconversion of glycerol. The investigated approach is a promising and environmentally safe route for value-added products from glycerol

A novel hybrid technique for short-term electricity price forecasting in deregulated electricity markets

Short-term electricity price forecasting is now crucial practice in deregulated electricity markets, as it forms the basis for maximizing the profits of the market participants. In this thesis, short-term electricity prices are forecast using three different predictor schemes, Artificial Neural Networks (ANNs), Support Vector Machine (SVM) and a hybrid scheme, respectively. ANNs are the very popular and successful tools for practical forecasting. In this thesis, a hidden-layered feed-forward neural network with back-propagation has been adopted for detailed comparison with other forecasting models. SVM is a newly developed technique that has many attractive features and good performance in terms of prediction. In order to overcome the limitations of individual forecasting models, a hybrid technique that combines Fuzzy-C-Means (FCM) clustering and SVM regression algorithms is proposed to forecast the half-hour electricity prices in the UK electricity markets. According to the value of their power prices, thousands of the training data are classified by the unsupervised learning method of FCM clustering. SVM regression model is then applied to each cluster by taking advantage of the aggregated data information, which reduces the noise for each training program. In order to demonstrate the predictive capability of the proposed model, ANNs and SVM models are presented and compared with the hybrid technique based on the same training and testing data sets in the case studies by using real electricity market data. The data was obtained upon request from APX Power UK for the year 2007. Mean Absolute Percentage Error (MAPE) is used to analyze the forecasting errors of different models and the results presented clearly show that the proposed hybrid technique considerably improves the electricity price forecasting.EThOS - Electronic Theses Online ServiceGBUnited Kingdo