• Alarm processing, diagnosis, and restoration. Expert systems and other knowledge-based systems have been used to address alarm processing, diagnosis, and restoration support at both the dispatch and control-center levels and the substation and generation-plant levels. When data and information concerning numerous events covering a large set of foreseen cases are available, an artificial neural network can be designed and trained with good results. The power industry trains control-center operators using software simulators, and some have experimented with intelligent tutoring systems.
• Forecasting. The level of load demand determines the amount of energy supply from power plants. Neural networks have been extensively used for forecasting tasks. They have also been used in price forecasting for electricity markets, primarily combined with fuzzy logic.
• Security assessment. Security assessment evaluates a power system's ability to face a set of contingencies in static and dynamic situations. Pattern recognition methods and artificial neural networks have been developed to meet these challenges.
• Planning and scheduling. These techniques are necessary in electrical networks and generation expansion planning as well as for several operation problems, such as unit commitment, optimal dispatch, hydro-thermal coordination, network reconfiguration, and maintenance scheduling. The techniques frequently used to solve these problems are computational intelligence and bioinspired techniques, including artificial neural networks, fuzzy systems, genetic algorithms, simulated annealing, tabu search, swarm intelligence, and ant colonies.
• Energy markets. While power-system engineering deals with the technical aspects of the grid, energy markets take into account the economic perspective of the power industry. Multiagent system, machine learning, data mining, and game theory techniques are used in solving problems in energy markets.
• Agents and data mining 4 are particularly interesting for the smart grid and energy markets, namely concerning agents for information mining.
• Group decision making and emotional computing 7 are important for tasks such as power system restoration and energy pricing.