Artificial intelligence systems in energy and renewable energy applications

Artificial intelligence systems 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 generalization at high speed.

Artificial intelligence systems have been used in diverse applications in control, robotics, pattern recognition, forecasting, medicine, power systems, manufacturing, optimization, signal processing, and social/psychological sciences. They are particularly useful in system modeling such as in implementing complex mappings and system identification. Artificial intelligence (AI) systems comprise areas like, expert systems, artificial neural networks, genetic algorithms, fuzzy logic and various hybrid systems, which combine two or more techniques.

Artificial intelligence techniques play an important role in modeling and prediction of the performance and control of energy and renewable energy processes. Results presented in various papers, are testimony to the potential of artificial intelligence as a design tool in many areas of energy and renewable energy engineering.

Artificial neural networks (ANNs) are collections of small individually interconnected processing units. Information is passed between these units along interconnections. An incoming connection has two values associated with it, an input value and a weight. The output of the unit is a function of the summed value. ANNs while implemented on computers are not programmed to perform specific tasks. Instead, they are trained with respect to data sets until they learn patterns used as inputs. Once they are trained, new patterns may be presented to them for prediction or classification. ANNs can automatically learn to recognize patterns in data from real systems or from physical models, computer programs, or other sources. They can handle many inputs and produce answers that are in a form suitable for designers.

Genetic algorithms are inspired by the way living organisms adapt to the harsh realities of life in a hostile world, i.e., by evolution and inheritance. The algorithm imitates in the process the evolution of population by selecting only fit individuals for reproduction. Therefore, a genetic algorithm is an optimum search technique based on the concepts of natural selection and survival of the fittest. It works with a fixed-size population of possible solutions of a problem, called individuals, which are evolving in time. A genetic algorithm utilizes three principal genetic operators: selection, crossover, and mutation.

Fuzzy logic is used mainly in control engineering. It is based on fuzzy logic reasoning which employs linguistic rules in the form of IF-THEN statements. Fuzzy logic and fuzzy control feature a relative simplification of a control methodology description. This allows the application of a “human language” to describe the problems and their fuzzy solutions. In many control applications, the model of the system is unknown or the input parameters are highly variable and unstable. In such cases, fuzzy controllers can be applied. These are more robust and cheaper than conventional PID controllers. It is also easier to understand and modify fuzzy controller rules, which not only use human operator’s strategy but, are expressed in natural linguistic terms.

Hybrid systems combine more than one of the technologies introduced above, either as part of an integrated method of problem solution, or to perform a particular task that is followed by a second technique, which performs some other task. For example, neuro-fuzzy controllers use neural networks and fuzzy logic for the same task, i.e., to control a process, whereas in another hybrid system a neural network may be used to derive some parameters and a genetic algorithm might be used subsequently to find an optimum solution to a problem.

For the modeling, prediction of performance and control of energy and renewable energy processes, analytic computer codes are often used. The algorithms employed are usually complicated involving the solution of complex differential equations. These programs usually require large computer power and need a considerable amount of time to give accurate predictions. Instead of complex rules and mathematical routines, artificial intelligence systems are able to learn the key information patterns within a multidimensional information domain. Data from energy and renewable energy processes being inherently noisy are good candidate problems to be handled with artificial intelligence systems.

Many of the energy and renewable energy problems are exactly the types of problems and issues for which artificial intelligence approach appear to be most applicable. In these models of computation, attempts are made to simulate the powerful cognitive and sensory functions of the human brain and to use this capability to represent and manipulate knowledge in the form of patterns. Based on these patterns neural networks for example model input-output functional relationships and can make predictions about other combinations of unseen inputs. Many of the artificial intelligence techniques have the potential for making better, quicker and more practical predictions than any of the traditional methods.

Artificial intelligence (AI) analysis is based on past history data of a system and is therefore likely to be better understood and appreciated by designers than other theoretical and empirical methods. AI may be used to provide innovative ways of solving design issues and will allow designers to get an almost instantaneous expert opinion on the effect of a proposed change in a design.