Enhanced Random Forest Model for Robust Short-Term Photovoltaic Power Forecasting Using Weather Measurements

Mohamed Massaoudi,Shady S Refaat,Mohamed Trabelsi,Fakhreddine S Oueslati,Ines Chihi,Lilia Sidhom

doi:10.3390/en14133992

Abstract

Short-term Photovoltaic (PV) Power Forecasting (STPF) is considered a topic of utmost importance in smart grids. The deployment of STPF techniques provides fast dispatching in the case of sudden variations due to stochastic weather conditions. This paper presents an efficient data-driven method based on enhanced Random Forest (RF) model. The proposed method employs an ensemble of attribute selection techniques to manage bias/variance optimization for STPF application and enhance the forecasting quality results. The overall architecture strategy gathers the relevant information to constitute a voted feature-weighting vector of weather inputs. The main emphasis in this paper is laid on the knowledge expertise obtained from weather measurements. The feature selection techniques are based on local Interpretable Model-Agnostic Explanations, Extreme Boosting Model, and Elastic Net. A comparative performance investigation using an actual database, collected from the weather sensors, demonstrates the superiority of the proposed technique versus several data-driven machine learning models when applied to a typical distributed PV system.

Highlights

Over the years, the exponential increase in global energy demand has become the leading cause of the rapid depletion of fossil fuels and increased Greenhouse Gas (GHG)emissions of conventional generators [1]
Despite the PV power output is sensitive to chaotic meteorological conditions, the proposed model has the potential to capture the trend of the PV power generation with dramatic variability
For a reliable and secure operation of power systems, this paper seeks to explore the problem of predicting PV power generation for efficiently manage the capacity of the intermittent asynchronous PV generators

Summary

Introduction

The exponential increase in global energy demand has become the leading cause of the rapid depletion of fossil fuels and increased Greenhouse Gas (GHG)emissions of conventional generators [1]. Solar Energy (SE) hold out the greatest promise for modern humankind among all RES, being free, clean, and abundantly available [3]. For these reasons, it keeps increasing its share in the energy-mix in the face of diminishing conventional fossil fuel energy sources and rising environmental protection concerns [3]. PV Power Forecasting (PPF) is a pivotal element for reliable power supply as it significantly reduces the sensitivity of energy systems to weather intermittency. PPF is mandatory for PV generators as it has a direct impact on the stability and reliability of the grid. Achieving accurate forecasting for PV power generation will facilitate the SE integration to the power system

Results

Discussion

Conclusion