Application of clustering analysis in the prediction of photovoltaic power generation based on neural network

Cheng, Ke; Guo, Lanhui; Wang, Y K; Zafar, Mudasar

doi:10.1088/1755-1315/93/1/012024

Cited by 8 publications

(9 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There is a vast literature on the impact of weather variables on PV generation [7,17,32]. Although there is a complex relationship between them, the input weather variables were directly categorized into two parts: primary weather variables (PWVs) and secondary weather variables (SWVs) [14].…”

Section: Similar Day Detection (Sdd)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Hybrid PV forecasting methods mainly focus on either specific or similar day identification [16][17][18][19][20][21][22]. Euclidean distance (ED)-based optimization [16], statistical analysis software (SPSS) [17], discrete wavelet transformation (DWT) [19], radial basis function [20], and wavelet packet distribution (WPD) [21] are the statistical techniques used for the determination of a specific type of day from historical days. Moreover, learning-based clustering techniques, such as support vector machines (SVMs) [19], k-nearest neighbors (k-NN) [17], and self-organizing map classification [14,22], are the major classification approaches that extract similar weather days.…”

Section: Introductionmentioning

confidence: 99%

“…Euclidean distance (ED)-based optimization [16], statistical analysis software (SPSS) [17], discrete wavelet transformation (DWT) [19], radial basis function [20], and wavelet packet distribution (WPD) [21] are the statistical techniques used for the determination of a specific type of day from historical days. Moreover, learning-based clustering techniques, such as support vector machines (SVMs) [19], k-nearest neighbors (k-NN) [17], and self-organizing map classification [14,22], are the major classification approaches that extract similar weather days. A strong similar day detection (SDD) method that deals with different impacts of weather variables for collecting homogenous PV generation profiles was developed [14].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Weather Data Mixing Models for Day-Ahead PV Forecasting in Small-Scale PV Plants

Acharya

Lee

2021

Energies

View full text Add to dashboard Cite

As a large number of small-scale PV plants have been deployed in distribution systems, generation forecasting of such plants has recently been gaining interest. Because the PV power mainly depends on weather conditions, it is important to accurately collect weather data for relevant PV sites to enhance PV forecasting accuracy. However, small-scale PV plants do not often have their own measuring apparatus to get historical weather data, so they have used weather datasets from relatively nearby weather data centers (WDCs). Therefore, these small-scale PV plants have difficulty delivering robust and reliable forecasting accuracy because of inappropriate predicted weather data from a distance. In this paper, two weather data mixing models are proposed: (a) inverse distance weighting (IDW), and (b) inverse correlation weighting (ICW). These models acquire adequate mixed weather data for the day-ahead generation forecasting for small-scale PV plants. Furthermore, the mixed weather data are collected using the geographic distance between the PV site and WDCs, or correlation between the PV generation and weather variables from nearby WDCs. Interestingly, the proposed ICW model outperforms when WDCs are located distant from the PV plants, whereas IDW performs well with the closer WDCs. The forecasting performance of the proposed mixing models was compared with those of the existing weather data collection methods.

show abstract

Section: Similar Day Detection (Sdd)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Weather Data Mixing Models for Day-Ahead PV Forecasting in Small-Scale PV Plants

Acharya

Lee

2021

Energies

View full text Add to dashboard Cite

show abstract

“…In [5], ANNs are used to predict residential building energy consumption. In [6], SVMs and ANNs are applied to predict heat and cooling demand in the non-residential sector, whereas in [7] ANNs and clustering are used to predict photovoltaic power generation. PCA is considered to analyze and forecast photovoltaic data in [8] and [9], meanwhile, in [10] and [11], SVM is used.…”

Section: Introductionmentioning

confidence: 99%

Enhancement of a Short-Term Forecasting Method Based on Clustering and kNN: Application to an Industrial Facility Powered by a Cogenerator

Vialetto

Noro

2019

Energies

View full text Add to dashboard Cite

In recent years, collecting data is becoming easier and cheaper thanks to many improvements in information technology (IT). The connection of sensors to the internet is becoming cheaper and easier (for example, the internet of things, IOT), the cost of data storage and data processing is decreasing, meanwhile artificial intelligence and machine learning methods are under development and/or being introduced to create values using data. In this paper, a clustering approach for the short-term forecasting of energy demand in industrial facilities is presented. A model based on clustering and k-nearest neighbors (kNN) is proposed to analyze and forecast data, and the novelties on model parameters definition to improve its accuracy are presented. The model is then applied to an industrial facility (wood industry) with contemporaneous demand of electricity and heat. An analysis of the parameters and the results of the model is performed, showing a forecast of electricity demand with an error of 3%.

show abstract

Influence of renewable energy power fluctuations on water electrolysis for green hydrogen production

Kojima

Nagasawa

Todoroki

et al. 2023

International Journal of Hydrogen Energy

214

View full text Add to dashboard Cite

Application of clustering analysis in the prediction of photovoltaic power generation based on neural network

Cited by 8 publications

References 5 publications

Weather Data Mixing Models for Day-Ahead PV Forecasting in Small-Scale PV Plants

Weather Data Mixing Models for Day-Ahead PV Forecasting in Small-Scale PV Plants

Enhancement of a Short-Term Forecasting Method Based on Clustering and kNN: Application to an Industrial Facility Powered by a Cogenerator

Influence of renewable energy power fluctuations on water electrolysis for green hydrogen production

Contact Info

Product

Resources

About