Artificial Intelligence (AI) is a term that in its broadest sense indicates the ability of a machine to perform the same kinds of functions that characterise human thought. The term AI has been applied to computer systems and programs which can perform tasks more complex than straightforward programming.

AI programs have the potential to make better, quicker and more practical predictions than our traditional methods. It will play a pivotal role in solar industries, through business intelligence and its ability to solve problems quicker than humans. These techniques are already being implemented by various researchers in solar energy applications. AI is making clean energy attractive for investors by offering great returns and significantly de-risking portfolios, and might play an important role in modelling and prediction of the performance of solar energy systems.

Artificial Intelligence systems comprise two major areas – EXPERT SYSTEMS and ARTIFICIAL NEURAL NETWROKS (ANNs)

1. Expert systems: Logic programs called expert systems allow computers to “make decisions” by interpreting data and selecting a decision among several alternatives. Expert systems take computers a step beyond straightforward programming, being based on a technique called rule-based inference, in which pre-established rule systems are used to process data. They are designed to solve complex problems by reasoning through bodies of knowledge, represented mainly as if-then rules rather than through conventional procedural code.
2. Artificial Neural Networks (ANNs): They are nonlinear statistical data modelling tools where the complex relationships between inputs and outputs are modelled or patterns are found. ANNs are collections of small individual interconnected processing units. Information is passed between these units along interconnections. An incoming connection has different values associated with it. 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 the patterns presented to them. Once they are trained, new patterns may be presented to them for prediction or classification.

Possible areas in solar energy where AI can be implemented:

1. 1. AI in prediction of solar radiation and weather forecasting     
      Developments in AI have become important in more accurately forecasting the weather and, in turn, improving renewable energy efficiency and accessibility. AI can also be useful in weather forecasting and for prediction of global solar radiation on horizontal surfaces. Accurate weather forecasting helps utilities make smart decisions about operations in severe weather conditions such as hail, thunderstorms and extreme wind. AI can analyse large volumes of historical and real-time data from satellites and weather stations to recognize patterns and predict weather that could impact solar production.
   2. AI for optimizing plant performance
      AI also can be used to maximize performance of power plants. For example, if data of ten power plants is available, maybe eight are performing at 99% and two at 95%. AI can analyse data—region, system, slopes, humidity, irradiance, manufacturer—to recognize anomalies or issues that a human may not.
   3. AI for predictive maintenance
      AI can be used for predictive maintenance by learning algorithms to spot inconsistencies and determine when a panel or an inverter is about to fail.The challenges associated with renewable energy’s variability are thought to be exactly the kind of issues for which AI is most applicable.