Short-Term Photovoltaic Power Output Prediction Based on k-Fold Cross-Validation and an Ensemble Model

Zhu, Ruijin; Guo, Weilin; Gong, Xuejiao

doi:10.3390/en12071220

Cited by 33 publications

(15 citation statements)

References 25 publications

(24 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similarly, different methods (models) can be used for very short-term or short-term solar (photovoltaic) power prediction. The examples are: using smart persistence and random forests for forecasts of photovoltaic energy production [31]; an ensemble model for short-term photovoltaic power forecasts [32]; hybrid method based on the variational mode decomposition technique, the deep belief network and the auto-regressive moving average model (for short-term solar power forecasts) [33]; a model which combines the wavelet transform, adaptive neuro-fuzzy inference system, and hybrid firefly and particle swarm optimization algorithm (for solar power forecasts) [34]; a physical hybrid artificial neural network for the 24 h ahead photovoltaic power forecast in microgrids [35]; a hybrid solar and wind energy forecasting system on short time scales [36].…”

Section: Related Workmentioning

confidence: 99%

Forecasting of 10-Second Power Demand of Highly Variable Loads for Microgrid Operation Control

et al. 2021

View full text Add to dashboard Cite

This paper addresses very short-term (10 s) forecasting of power demand of highly variable loads. The main purpose of this study is to develop methods useful for this type of forecast. We have completed a comprehensive study using two different time series, which are very difficult to access in practice, of 10 s power demand characterized by big dynamics of load changes. This is an emerging and promising forecasting research topic, yet to be more widely recognized in the forecasting research community. This problem is particularly important in microgrids, i.e., small energy micro-systems. Power demand forecasting, like forecasting of renewable power generation, is of key importance, especially in island mode operation of microgrids. This is due to the necessity of ensuring reliable power supplies to consumers. Inaccurate very short-term forecasts can cause improper operation of microgrids or increase costs/decrease profits in the electricity market. This paper presents a detailed statistical analysis of data for two sample low voltage loads characterized by large variability, which are located in a sewage treatment plant. The experience of the authors of this paper is that very short-term forecasting is very difficult for such loads. Special attention has been paid to different forecasting methods, which can be applied to this type of forecast, and to the selection of explanatory variables in these methods. Some of the ensemble models (eight selected models belonging to the following classes of methods: random forest regression, gradient boosted trees, weighted averaging ensemble, machine learning) proposed in the scope of choice of methods sets constituting the models set are unique models developed by the authors of this study. The obtained forecasts are presented and analyzed in detail. Moreover, qualitative analysis of the forecasts obtained has been carried out. We analyze various measures of forecasts quality. We think that some of the presented forecasting methods are promising for practical applications, i.e., for microgrid operation control, because of their accuracy and stability. The analysis of usefulness of various forecasting methods for two independent time series is an essential, very valuable element of the study carried out. Thanks to this, reliability of conclusions concerning the preferred methods has considerably increased.

show abstract

Section: Related Workmentioning

confidence: 99%

Forecasting of 10-Second Power Demand of Highly Variable Loads for Microgrid Operation Control

et al. 2021

View full text Add to dashboard Cite

show abstract

“…As mentioned before, a k -fold cross-validation technique is utilized in order to examine generalization performances of designed MLPs. First step for performing k -fold cross-validation is division of the entire dataset into k parts [77,79]. Than, one part of the divided dataset is used for MLP testing, while remaining parts are used for MLP training.…”

Section: Dataset Descriptionmentioning

confidence: 99%

Genetic Algorithm Approach to Design of Multi-Layer Perceptron for Combined Cycle Power Plant Electrical Power Output Estimation

et al. 2019

View full text Add to dashboard Cite

In this paper a genetic algorithm (GA) approach to design of multi-layer perceptron (MLP) for combined cycle power plant power output estimation is presented. Dataset used in this research is a part of publicly available UCI Machine Learning Repository and it consists of 9568 data points (power plant operating regimes) that is divided on training dataset that consists of 7500 data points and testing dataset containing 2068 data points. Presented research was performed with aim of increasing regression performances of MLP in comparison to ones available in the literature by utilizing heuristic algorithm. The GA described in this paper is performed by using mutation and crossover procedures. These procedures are utilized for design of 20 different chromosomes in 50 different generations. MLP configurations that are designed with GA implementation are validated by using Bland - Altman (B-A) analysis. By utilizing GA, MLP with five hidden layers of 80,25,65,75 and 80 nodes, respectively, is designed. For aforementioned MLP, k - fold cross-validation is performed in order to examine its generalization performances. The Root Mean Square Error ( R M S E ) value achieved with aforementioned MLP is 4.305 , that is significantly lower in comparison with MLP presented in available literature, but still higher than several complex algorithms such as KStar and tree based algorithms.

show abstract

“…In the last years, machine learning (ML) techniques have been widely adopted to estimate the long-term performances, and predict the output power, of PV plants [9,10]. Theocharides et al [11] assessed the PV generation using three ML techniques, such as artificial neural network (ANN), support vector machine (SVM), and regression tree.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Based Approaches for Modeling the Output Power of Photovoltaic Array in Real Outdoor Conditions

Malvoni

Chaibi

2020

Electronics

View full text Add to dashboard Cite

It is important to investigate the long-term performances of an accurate modeling of photovoltaic (PV) systems, especially in the prediction of output power, with single and double diode models as the configurations mainly applied for this purpose. However, the use of one configuration to model PV panel limits the accuracy of its predicted performances. This paper proposes a new hybrid approach based on classification algorithms in the machine learning framework that combines both single and double models in accordance with the climatic condition in order to predict the output PV power with higher accuracy. Classification trees, k-nearest neighbor, discriminant analysis, Naïve Bayes, support vector machines (SVMs), and classification ensembles algorithms are investigated to estimate the PV power under different conditions of the Mediterranean climate. The examined classification algorithms demonstrate that the double diode model seems more relevant for low and medium levels of solar irradiance and temperature. Accuracy between 86% and 87.5% demonstrates the high potential of the classification techniques in the PV power predicting. The normalized mean absolute error up to 1.5% ensures errors less than those obtained from both single-diode and double-diode equivalent-circuit models with a reduction up to 0.15%. The proposed hybrid approach using machine learning (ML) algorithms could be a key solution for photovoltaic and industrial software to predict more accurate performances.

show abstract

Short-Term Photovoltaic Power Output Prediction Based on k-Fold Cross-Validation and an Ensemble Model

Cited by 33 publications

References 25 publications

Forecasting of 10-Second Power Demand of Highly Variable Loads for Microgrid Operation Control

Forecasting of 10-Second Power Demand of Highly Variable Loads for Microgrid Operation Control

Genetic Algorithm Approach to Design of Multi-Layer Perceptron for Combined Cycle Power Plant Electrical Power Output Estimation

Machine Learning Based Approaches for Modeling the Output Power of Photovoltaic Array in Real Outdoor Conditions

Contact Info

Product

Resources

About