This paper presents the capability of an emerging swarm intelligence technique for power loss minimization known as the Artificial Bee Colony (ABC) used in the context of an Alternative Load Flow Analysis (LFA) technique (ABC-LFA) for the solution of a power systems network. Studies are performed considering the effect of an important parameter of the ABC, the “maxcycle” on the LFA process; experiments are conducted by applying the ABC-LFA to the Western System Coordinated Council (WSCC) 3-machine 9- bus power system and a section of the Nigerian 132-kV power transmission network Port-Harcourt Region (NPHC-132), and the results reported. The results indicate that increasing the value of the ABC “maxcycle” parameter has a pronounced effect on the results obtained by the ABC-LFA. The results also indicate the sensitivity of the ABC to low values of maxcycle parameter.
The primary aim of this study is to develop a model for estimation of the cooling requirement of residential rooms. Fuzzy logic was employed to model four input variables (window area (m 2), roof area (m 2), external wall area (m 2) and internal load (Watt). The algorithm of the inference engine applied sets of 81 linguistic rules to generate the output variable in Cooling Load rating. A paired t-test was carried out using SPSS version 20 package, with the results of human professionals' calculations for each assessed rooms compared with the model generated Cooling Load capacities for the same set of variables. The human calculation and model results were observed to be strongly correlated (r=0.880, p<0.001) with no significant difference between the two sets of variables (t19=-1.697, p>0.001). On the average, human calculation values were 221.5 points lower than model calculated values (at 95% confidence interval [-447.55, 4.50]). The study proposed a model to size the cooling capacity of residential rooms. The model is capable of providing results comparable to that of human professionals in the application area. It is simple and can find its usefulness among building consultants/professionals and home owners.
Diverse opinions exist in the time series analysis of energy and related indices, difference in methodology, sample size, and time variation. This paper will make a conscious effort to converge the divergent outlooks. To accomplish this essential task, five energy indices consisting of energy consumption (EC), gross domestic product (GDP), carbon dioxide emission (CDE), the human development index (HDI), and oil price (OP) were selected. Two analytical methods were adopted, namely logarithmic and normalized techniques, which are designed to complement each other in drawing unfalsified statistical inference concerning the causality between the energy indices. The methods were subjected to four statistical tests and analyses: the augmented Dickey-Fuller, cointegration, pairwise Granger causality, and vector error correction model (VECM). Irrespective of prevailing challenges, both logarithmic and normalized techniques unanimously filtered out causalities. This consisted of neural flow between oil price and energy consumption, gross domestic product and carbon dioxide emission, and energy consumption and the human development index, unidirectional flow between energy consumption and the human development index, oil price and energy consumption, gross domestic product and carbon dioxide emission, and the human development index and oil price, whereas a normalized technique established bidirectional flow between gross domestic product and the human development index, and the human development index and oil price. Pertinently, the research suggests appropriate policies that will generate sustainable development in all the causal directions. Assiduously, the overwhelming agreement between both techniques at the 0.05 level is recommended for further validation with more modern econometric tests.
The classical minimization of power losses in transmission lines is dominated by artificial intelligence techniques, which do not guarantee global optimum amidst local minima. Revolutionary and evolutionary techniques are encumbered with sophisticated transformations, which weaken the techniques. Power loss minimization is crucial to the efficient design and operation of power transmission lines. Minimization of losses is one way to meet steady grid supply, especially at peak demand. Thus, this paper has presented a gradient technique to obtain optimal variables and values from the power loss model, which efficiently minimizes power losses by modifying the traditional power loss model that combines Ohm and Corona losses. Optimality tests showed that the unmodified model does not support the minimization of power losses on transmission lines as the Hessian matrix portrayed the maximization of power losses. However, the modified model is consistent with the gradient method of optimization, which yielded optimum variables and values from the power loss model developed in this study. The unmodified (modified) models for Bujagali-Kawanda 220 kV and Masaka West-Mbarara North 132 kV transmission lines in Uganda showed maximum power losses of 0.406 (0.391) and 0.452 (0.446) kW/km/phase respectively. These results indicate that the modified model is superior to the unmodified model in minimizing power losses in the transmission lines and should be implemented for the efficient design and operation of power transmission lines within and outside Uganda for the same transmission voltages.
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