Using Artificial Intelligence to create predictive systems

Are you interested in Artificial Intelligence?

AI was coined in the mid 1950s, and was heavily funded by the Department of Defense for many years. Unfortunately, the practitioners at the time were overly optimistic and failed to identify some of the difficulties that they eventually faced. By the mid 1970s, funding was largely cut in favor of more promising projects.

In the early 1980s, AI resurged with the commerical success of a branch called Expert Systems. But again, there were issues, and AI fell back into hibernation and had followings mostly in research institutes.

By the 1990s, AI came back again, especially focused on analyzing information and data mining. In 1997, Deep Blue became the first chess computer to beat the world champion Garry Kasparov. In the 2000s, the Defense Advanced Research Projects Agency developed successful autonomous vehicle programs known as the DARPA Grand Challenge and Urban Challenge that could navigate both desert trails (a rugged 131 mile track was used in the demonstration) and urban environments (a 55 mile track demonstrated), all the while obeying traffic laws, hazards, other vehicles, and even pedestrians. We see these technologies in our cars today.

Applying Neural Networks to Investigate Electrical Power Plant Cooling-Water Discharge Temperature describes using AI for data mining and forecasting. The focus is on using AI (in this case, a branch known as Neural Networks) to create predictive algorithms dealing with the cooling water in electric power production. My interest in part was in line with the Environmental Protection Agency’s desire to protect the manatees who make a home in Tampa Bay.

Happy reading! By the way, I’d love to hear your feedback if you do decide to read.




This thesis investigates neural computing applied to electrical power plant cooling water system forecasting. The target system is a coal-fired, base-load electrical power generation facility owned and operated by the Tampa Electric Company in Apollo Beach, Florida. During the process of converting chemical energy contained in coal to heat energy and finally to electrical energy, excess heat energy is created and must be dissipated. The excess heat dissipation occurs by way of Tampa Bay, a large body of salt water directly navigable to the Gulf of Mexico.

The Environmental Protection Agency has established water temperature discharge guidelines and restrictions in order to protect the surrounding habitat. Current plant operations dictate unwritten heuristic approaches used to reduce the opportunity for temperature violations. The value in this work is twofold. First, the study attempts to uncover features that affect the water temperature as it leaves the Big Bend facility.  These features, once identified, are then used to understand the characteristics of the plant as they relate to the water temperature discharge.

Neural computing techniques are used in this study because most of the variables  exhibit interactions that are difficult to discern and categorize through other approaches. The pattern recognition facility of neural computing is especially important to this investigation. Intermediate neural network architectures are designed for proving specific feature interactions, and these models contribute to the final system architecture mostly by demonstrating preprocessing requirements.

The resulting artificial neural network architecture and associated preprocessing accommodates time delay features and produces results to within 1.0 degree Fahrenheit.  The final architecture is a Cascade Correlation hidden layer model, composed of 28 input nodes and 1 output node.