Design of an Automatic Control Algorithm for Energy-Efficient ...
Design of an Automatic Control Algorithm for Energy-Efficient ...
Design of an Automatic Control Algorithm for Energy-Efficient ...
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12 <strong>Control</strong>ler evaluation 118<br />
a good indication <strong>for</strong> it.<br />
The sensor inputs <strong>an</strong>d other settings are taken from the Heat-Up simulation test<br />
case, presented later in this chapter in Section 12.4. All estimated values are set to zero.<br />
The optimum has been found by a long-time simulation 2 . After 100 seconds, the ambient<br />
temperature is lowered by 5 degrees to show how the optimiser reacts a (sudden) ch<strong>an</strong>ge.<br />
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Figure 12.2: Convergence plot <strong>of</strong> optimisation runs with a static problem.<br />
The result <strong>of</strong> 10 test-runs is shown in Figure 12.2. The stochastic nature <strong>of</strong> the<br />
evolutionary algorithm is seen by the fact that all solutions are different. There<strong>for</strong>e, the<br />
me<strong>an</strong> value <strong>an</strong>d the st<strong>an</strong>dard deviation are introduced to show the average algorithm<br />
results. The fitness <strong>of</strong> the best possible solution ch<strong>an</strong>ges with the environment as it c<strong>an</strong><br />
be observed at the ch<strong>an</strong>ge <strong>of</strong> the ambient temperature.<br />
The optimum is not reached by <strong>an</strong>y <strong>of</strong> the lines, but all <strong>of</strong> the results converge<br />
to it. This happens quite fast in the beginning. After 10 seconds (5 optimiser runs) the<br />
me<strong>an</strong> only is 10 points away from the best possible fitness. This equals approximately<br />
4% <strong>of</strong> the worst possible score .<br />
After the sudden ch<strong>an</strong>ge at , the peak is even smaller <strong>an</strong>d 10 seconds later<br />
the me<strong>an</strong> only is 4 points away from the optimum. This small difference will usually not<br />
2The optimiser will always converge to the optimum, if one c<strong>an</strong> be found, after <strong>an</strong> infinite time due to<br />
its heuristic nature.