Predicting electricity demands

electricity
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Research published in the International Journal of Energy Technology and Policy shows how a neural network can be trained with a genetic algorithm to forecasting short-term demands on electricity load. Chawalit Jeenanunta and Darshana Abeyrathna of Thammasat University, in Thani, Thailand, explain that it is critical for electricity producers to be able to estimate how much demand there will be on their systems in the next 48 hours. Without such predictions, there will inevitably be shortfalls in power generation when demand is higher than estimated or energy and resources wasted if demand is lower than expected.

The team has used data from the electricity generating authority of Thailand (EGAT) to train a neural network via a . The results are compared with the more conventional back-propagation approach to prediction and show that the system is much better and predict the rise and falls in electricity demand. The genetic algorithm neural network (GANN) approach takes about 30 minutes to train for prediction compared with 1 minute for back-propagation training of a . However, the added value of much more accurate predictions far outweighs this additional time and effort.


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More information: Chawalit Jeenanunta et al. Neural network with genetic algorithm for forecasting short-term electricity load demand, International Journal of Energy Technology and Policy (2019). DOI: 10.1504/IJETP.2019.098957
Provided by Inderscience
Citation: Predicting electricity demands (2019, April 17) retrieved 23 October 2019 from https://phys.org/news/2019-04-electricity-demands.html
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Apr 17, 2019
A reminder: neural networks aren't all that smart. See http://aiweirdness.com/ for a very snarky view of how neural networks behave.

I have the results from attempts to do the same for water consumption predictions. Human beings almost always did better than the AI. The AI will also need inputs from things like the weather, the day of the week, time of year and so forth.

This is a lot more difficult than it looks.

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