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Household Power Demand Prediction Using Evolutionary Ensemble Neural Network Pool with Multiple Network Structures.

Songpu Ai1, Antorweep Chakravorty2, Chunming Rong3

  • 1Department of Electrical Engineering and Computer Science, University of Stavanger, 4036 Stavanger, Norway. songpu.ai@uis.no.

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|February 13, 2019
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Summary

This study introduces an evolutionary ensemble neural network pool (EENNP) to automatically optimize home energy management systems (HEMS). The EENNP method improves household power demand prediction and data refilling, outperforming traditional approaches.

Keywords:
HEMSartificial neural networkdemand predictionensemble learningevolutionary algorithmgated recurrent unitlong short-term memorymachine learningmissing datasmart sensor

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Area of Science:

  • Energy Systems Engineering
  • Artificial Intelligence in Energy
  • Internet of Things (IoT) Applications

Background:

  • Increasingly complex electric environments in low-voltage microgrids due to technological advancements in energy and IoT.
  • The need for effective Home Energy Management Systems (HEMS) to integrate and manage household energy resources for decentralized systems.
  • The critical role of domestic power demand prediction for HEMS, load balancing, and smart energy solutions.

Purpose of the Study:

  • To propose an automated method for selecting optimal neural network configurations and initializations for energy prediction.
  • To develop an evolutionary ensemble neural network pool (EENNP) for efficient HEMS.
  • To evaluate the performance of EENNP in optimizing network configurations, forecasting household power demand, and refilling missing data.

Main Methods:

  • Development of an evolutionary ensemble neural network pool (EENNP) for automatic configuration and initialization of neural networks.
  • Experimental evaluation in three scenarios: optimizing network configurations, single household power demand forecasting, and missing data imputation.
  • Investigation of the impact of evolutionary parameters on model performance.

Main Results:

  • The EENNP method successfully generated a pool of well-performing neural networks with optimized configurations and initializations.
  • Optimized network configurations using EENNP showed comparable results to manual optimization.
  • Household demand prediction and missing data refilling using EENNP significantly outperformed naive and simple predictors.

Conclusions:

  • The proposed EENNP method provides an effective automated approach for developing high-performing neural networks for HEMS.
  • EENNP enhances the efficiency and accuracy of domestic power demand prediction and data imputation in smart energy systems.
  • This approach addresses the limitations of empirical and random initialization methods in current energy prediction research.