Showing papers by "Soteris A. Kalogirou published in 2001"

Artificial neural networks in renewable energy systems applications: a review

Abstract: Artificial neural networks are widely accepted as a technology offering an alternative way to tackle complex and ill-defined problems. They can learn from examples, are fault tolerant in the sense that they are able to handle noisy and incomplete data, are able to deal with non-linear problems and, once trained, can perform prediction and generalisation at high speed. They have been used in diverse applications in control, robotics, pattern recognition, forecasting, medicine, power systems, manufacturing, optimisation, signal processing and social/psychological sciences. They are particularly useful in system modelling such as in implementing complex mappings and system identification. This paper presents various applications of neural networks mainly in renewable energy problems in a thematic rather than a chronological or any other order. Artificial neural networks have been used by the author in the field of solar energy; for modelling and design of a solar steam generating plant, for the estimation of a parabolic trough collector intercept factor and local concentration ratio and for the modelling and performance prediction of solar water heating systems. They have also been used for the estimation of heating loads of buildings, for the prediction of air flow in a naturally ventilated test room and for the prediction of the energy consumption of a passive solar building. In all those models a multiple hidden layer architecture has been used. Errors reported in these models are well within acceptable limits, which clearly suggest that artificial neural networks can be used for modelling in other fields of renewable energy production and use. The work of other researchers in the field of renewable energy and other energy systems is also reported. This includes the use of artificial neural networks in solar radiation and wind speed prediction, photovoltaic systems, building services systems and load forecasting and prediction.

Design and Construction of a Lithium Bromide Water Absorption Refrigerator

Abstract: The objective of this work is to design and construct a lithium bromide–water (LiBr-H2O) absorption refrigerator with a nominal capacity of 1 kW. Absorption refrigerators are machines, which produce cooling by using heat energy, and have no moving parts. The various stages of design are presented including the design of the evaporator, absorber, solution heat exchanger, generator and condenser. The major problem faced during the design stage was the calculation of the heat transfer coefficient (U-value) of the various components. Single-pass vertical-tube heat exchangers have been used for the absorber and for the evaporator. The solution heat exchanger was designed as a single-pass annulus heat exchanger. The condenser and the generator were designed using horizontal tube heat exchangers. The condenser handles pure water vapour and adequate equations exist for the determination of the U-value. A pool-boiling generator has been employed and its U-value was estimated experimentally as published work on this area is limited.

Artificial neural networks for predicting air flow in a naturally ventilated test room

Abstract: The objective of this research is to investigate air flow distribution inside a light weight test room which is naturally ventilated using artificial neural networks The test room is situated in a relatively sheltered location and is ventilated through adjustable louvres Indoor air temperature and velocity are measured at four locations and six different levels The outside local temperature, relative humidity, wind velocity and direction are also monitored The collected data are used to predict the air flow across the test room A multi-layer feedforward neural network was employed with three hidden slabs Satisfactory results with correlation coefficients equal to 0985 and 0897, for the indoor temperature and combined velocity, respectively have been obtained when unknown input data, not used for network training, were used as input Both values are satisfactory especially if the fact that combined velocity readings were very unstable is considered The work presented in this paper primarily aims t

Artificial Neural Networks for Predicting the Pressure Coefficients in a Naturally Ventilated Test Room

Abstract: The objective of this work is to investigate the possibility of using artificial neural networks for the prediction of air pressure coefficients across the openings in a light weight single-sided naturally ventilated test room. Experimental values have been used for the training of the network. The outside local temperature, wind velocity and direction are monitored. The pressure coefficients at the top and bottom of the openings have been estimated from the recorded data of air pressures and velocities across the openings together with indoor air temperatures. The collected data together with the air heater load and a factor indicating whether the opening is in the windward (1) or leeward (0) direction are used as input to the neural network and the estimated pressure coefficients as the output. A general regression neural network (GRNN) was employed with one hidden slab. The training was performed with satisfactory accuracy and correlation coefficients of 0.9539 and 0.9325 have been obtained for the two coefficients respectively. Satisfactory results have been obtained when unknown data were used as input to the network with correlation coefficients of 0.9575 and 0.9320 respectively.