An agent-based fuzzy-neural approach for precise energy consumption forecasting

Precise energy consumption forecasting is of major importance to define the future energy consumption of a given region. However, it is not easy to contend with the uncertainty of the long-term energy consumption. In order to effectively forecast the long-term energy consumption, an agent-based fuzzy-neural approach is proposed in this study. In the proposed methodology, a group of agents is formed. These agents configure their own fuzzy neural networks to forecast the long-term energy consumption based on the settings. A collaboration mechanism governed by the centralized efficient P2P communication is therefore established. To facilitate the collaboration process and to derive a single representative value from these forecasts, the fuzzy group learning tree technique is used. The agent-based fuzzy-neural approach takes into account the different points of view in a more efficient way, and therefore the results obtained are more comprehensive and more in-depth. The effectiveness of the proposed methodology is illustrated with a case study.