Enhanced Deep Belief Network Based on Ensemble Learning and Tree-Structured of Parzen Estimators: An Optimal Photovoltaic Power Forecasting Method

Mohamed Massaoudi,Shady S Refaat,Ines Chihi,Haitham Abu-Rub,Fakhreddine S Oueslati,Mohamed Trabelsi

doi:10.1109/access.2021.3125895

Abstract

The random fluctuation and non-uniformity of Photovoltaic (PV) power generation greatly affect the power grids’ stability and operation. This paper addresses the high volatility of PV power by proposing a precise and reliable ensemble learning model for short-term PV power generation forecasting. The proposed forecasting tool incorporates a base model and meta-model layers. The first-layer base learner combines extreme learning machines, extremely randomized trees, k-nearest neighbor, and mondrian forest models. The meta-model layer exploits deep belief network to generate the final outputs. The hyper-parameters of the proposed stacking ensemble are carefully tuned using the tree-structured of parzen estimators algorithm to achieve top-notch predictive performance. The proposed model is thoroughly assessed through an empirical study using a real data set from Australia. The simulation results confirm the performance superiority of the proposed model over the existing forecasting models with the lowest average root mean square error and mean absolute percentage error of 3.88kW and 2.30%, respectively.

Highlights

W ITH the rapid growth of Photovoltaic (PV) capacity, PV Power Forecasting (PVPF) presents an effective solution to cope with the unexpected changes of weather conditions [1]
The learning environment setup is run on a Google Colaboratory, a free cloud service supported by Google with Graphics Processing Unit (GPU) enabled
This paper proposes a novel PVPF framework named Enhanced Deep Belief Network (EDBN)

Summary

Introduction

W ITH the rapid growth of Photovoltaic (PV) capacity, PV Power Forecasting (PVPF) presents an effective solution to cope with the unexpected changes of weather conditions [1]. The PV forecasts allow the compensation of the deficit in PV power generation from alternative sources. The PVPF copes with the weather outliers and provides information integrity to customers and energy suppliers. Accurate forecasting of PV power output is crucial for energy control and management in smart grid systems, especially when the well-known concept adopted by customers is the “fit and forget” approach [1]. The conditional hierarchical relations between heterogeneous generation sources and demand need an accurate forecasting model to prevent blackouts and system failures [1]. More sophisticated forecasting methods are highly needed to meet the technical requirements of actual PV plants

Methods

Results

Conclusion