Abstract: A theoretical study is conducted to design and explore
the effect of different parameters such as heat loads, the tube size of
piping system, wick thickness, porosity and hole size on the
performance and capability of a Loop Heat Pipe(LHP). This paper
presents a steady state model that describes the different phenomena
inside a LHP. Loop Heat Pipes(LHPs) are two-phase heat transfer
devices with capillary pumping of a working fluid. By their original
design comparing with heat pipes and special properties of the
capillary structure, they-re capable of transferring heat efficiency for
distances up to several meters at any orientation in the gravity field,
or to several meters in a horizontal position. This theoretical model is
described by different relations to satisfy important limits such as
capillary and nucleate boiling. An algorithm is developed to predict
the size of the LHP satisfying the limitations mentioned above for a
wide range of applied loads. Finally, to assess and evaluate the
algorithm and all the relations considered, we have used to design a
new kind of LHP to recover the heat from the exhaust of an actual
Gas Turbine. By finding the results, it showed that we can use the
LHP as a very high efficient device to recover the heat even in high
amount of loads(exhaust of a gas turbine). The sizes of all parts of the
LHP were obtained using the developed algorithm.
Abstract: One of the most important requirements for the
operation and planning activities of an electrical utility is the
prediction of load for the next hour to several days out, known as
short term load forecasting. This paper presents the development of
an artificial neural network based short-term load forecasting model.
The model can forecast daily load profiles with a load time of one
day for next 24 hours. In this method can divide days of year with
using average temperature. Groups make according linearity rate of
curve. Ultimate forecast for each group obtain with considering
weekday and weekend. This paper investigates effects of temperature
and humidity on consuming curve. For forecasting load curve of
holidays at first forecast pick and valley and then the neural network
forecast is re-shaped with the new data. The ANN-based load models
are trained using hourly historical. Load data and daily historical
max/min temperature and humidity data. The results of testing the
system on data from Yazd utility are reported.
Abstract: Load forecasting has become in recent years one of the major areas of research in electrical engineering. Most traditional forecasting models and artificial intelligence neural network techniques have been tried out in this task. Artificial neural networks (ANN) have lately received much attention, and a great number of papers have reported successful experiments and practical tests. This article presents the development of an ANN-based short-term load forecasting model with improved generalization technique for the Regional Power Control Center of Saudi Electricity Company, Western Operation Area (SEC-WOA). The proposed ANN is trained with weather-related data and historical electric load-related data using the data from the calendar years 2001, 2002, 2003, and 2004 for training. The model tested for one week at five different seasons, typically, winter, spring, summer, Ramadan and fall seasons, and the mean absolute average error for one hour-ahead load forecasting found 1.12%.