Category: SOLAR


As was indicated before, the usual approach in solar process economics is to use a life cycle cost method, which takes into consideration all future expenses and compares the future costs with today’s costs. Such a comparison is done by dis­counting all costs expected in the future to the common basis of present value or […]

Life Cycle Costing

Life cycle analysis, in fact, reflects the benefits accumulated by the use of solar energy against the fuel savings incurred. Compared to conventional fossil fuel systems, solar energy systems have relatively high initial cost and low operat­ing cost, whereas the opposite is true for conventional systems. Therefore, in a naive selection, based on the initial […]


The right proportion of solar to auxiliary energy is determined by economic analysis. There are various types of such analysis, some simple and others more complicated, based on thermoeconomics. The economic analysis of solar energy systems is carried out to determine the least cost of meeting the energy needs, considering both solar and non-solar alternatives. […]

Solar Economic Analysis

Although the resource of a solar energy system, that is, the solar irradiation, is free, the equipment required to collect it and convert it to useful form (heat or electricity) has a cost. Therefore, solar energy systems are generally char­acterized by high initial cost and low operating costs. To decide to employ a solar energy […]


Simulations are powerful tools for solar energy systems design, offering a number of advantages, as outlined in the previous sections. However, there are limits to their use. For example, it is easy to make mistakes, such as assum­ing wrong constants and neglect important factors. As with other engineering calculations, a high level of skill and […]

Hybrid Systems

Hybrid systems are systems that combine two or more artificial intelligence tech­niques to perform a task. The classical hybrid system is the neuro-fuzzy control, whereas other types combine genetic algorithms and fuzzy control or artificial neural networks and genetic algorithms as part of an integrated problem solution or to perform specific, separate tasks of the […]


Fuzzy inference is a method that interprets the values in the input vector and, based on some sets of rules, assigns values to the output vector. In fuzzy logic, the truth of any statement becomes a matter of a degree. Fuzzy inference is the process of formulating the mapping from a given input to an […]


Fuzzy sets and fuzzy operators are the subjects and verbs of fuzzy logic. While the differential equations are the language of conventional control, if-then rules, which determine the way a process is controlled, are the language of fuzzy control. Fuzzy rules serve to describe the quantitative relationship between variables in linguistic terms. These if-then rule […]