Deep Learning with Loss Ensembles for Solar Power Prediction in Smart Cities
Journal article
Hajiabadi, M., Farhadi, M., Babaiyan, V. and Estebsari, A. (2020). Deep Learning with Loss Ensembles for Solar Power Prediction in Smart Cities. Smart Cities. 3, pp. 842-852. https://doi.org/10.3390/smartcities3030043
Authors | Hajiabadi, M., Farhadi, M., Babaiyan, V. and Estebsari, A. |
---|---|
Abstract | The demand for renewable energy generation, especially photovoltaic (PV) power generation, has been growing over the past few years. However, the amount of generated energy by PV systems is highly dependent on weather conditions. Therefore, accurate forecasting of generated PV power is of importance for large-scale deployment of PV systems. Recently, machine learning (ML) methods have been widely used for PV power generation forecasting. A variety of these techniques, including artificial neural networks (ANNs), ridge regression, K-nearest neighbour (kNN) regression, decision trees, support vector regressions (SVRs) have been applied for this purpose and achieved good performance. In this paper, we briefly review the most recent ML techniques for PV energy generation forecasting and propose a new regression technique to automatically predict a PV system’s output based on historical input parameters. More specifically, the proposed loss function is a combination of three well-known loss functions: Correntropy, Absolute and Square Loss which encourages robustness and generalization jointly. We then integrate the proposed objective function into a Deep Learning model to predict a PV system’s output. By doing so, both the coefficients of loss functions and weight parameters of the ANN are learned jointly via back propagation. We investigate the effectiveness of the proposed method through comprehensive experiments on real data recorded by a real PV system. The experimental results confirm that our method outperforms the state-of-the-art ML methods for PV energy generation forecasting. |
Keywords | energy forecasting; photovoltaic systems; machine learning; artificial neural network; deep learning |
Year | 2020 |
Journal | Smart Cities |
Journal citation | 3, pp. 842-852 |
Publisher | MDPI |
Digital Object Identifier (DOI) | https://doi.org/10.3390/smartcities3030043 |
Web address (URL) | https://www.mdpi.com/2624-6511/3/3/43 |
Publication dates | |
Online | 07 Aug 2020 |
Publication process dates | |
Accepted | 04 Aug 2020 |
Deposited | 07 Aug 2020 |
Accepted author manuscript | License File Access Level Open |
https://openresearch.lsbu.ac.uk/item/8q42z
Download files
Accepted author manuscript
Deep Learning with Loss Ensembles for Solar Power Prediction in Smart Cities.pdf | ||
License: CC BY 4.0 | ||
File access level: Open |
145
total views102
total downloads3
views this month10
downloads this month