Embedded Agents for District Heating Management
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
Neural Networks: A Comprehensive Foundation (3rd Edition)
Neural Networks: A Comprehensive Foundation (3rd Edition)
Heat Consumption Prediction with Multiple Hybrid Models
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part II: Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living
SOM-based selection of monitored consumers for demand prediction
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
Handling incomplete data using evolution of imputation methods
ICANNGA'09 Proceedings of the 9th international conference on Adaptive and natural computing algorithms
| Hi-index | 0.00 |
Numerous techniques of artificial intelligence have been used for building prediction models. One of such tasks is the prediction of heat consumption in a district heating system. Not only is it required for ensuring sufficient heat production, but also it is necessary to avoid substantial heat loss due to overestimated demand for heat. The work presents the use of multilayer perceptrons for building prediction models. However, instead of building prediction models based on artificial neural networks only, hybrid approach is considered and evaluated. Evolutionary approach used to combine neural networks and a number of simple methods into hybrid prediction models is presented. Such models are developed for groups of consumers sharing similar thermal properties identified by self-organising map. It has been shown that by combining neural networks with simple predictive strategies lower prediction error rates can be achieved than in case of using neural networks only.