Intra-day Solar Irradiance Forecasting Based on Artificial Neural Networks

Theocharides, Spyros; Kynigos, Marios; Theristis, Marios; Makrides, George; Georghiou, George E.

doi:10.1109/pvsc40753.2019.8980480

Cited by 7 publications

(10 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Please note that CVRMSE results are particularly high due to nighttime 0 values that decrease the mean of the active power production. These results are on a par with the state-of-the-art research on solar forecasting [13,14].…”

supporting

confidence: 77%

“…The higher penetration of solar energy gives an ever-growing importance to increasingly accurate forecasting [12]. To capture the local circumstances and phenomena of each individual plant, Machine Learning (ML) provides a widely used solution [13,14]. These algorithms on the day-ahead market are trained on historical Numerical Weather Prediction data and other cyclical variables, where the target is the active power production at the point of common coupling.…”

Section: Solar Forecasting Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Optimized Charge Controller Schedule in Hybrid Solar-Battery Farms for Peak Load Reduction

Barta

Pasztor

Prava³

2021

Energies

View full text Add to dashboard Cite

The goal of this paper is to optimally combine day-ahead solar and demand forecasts for the optimal battery schedule of a hybrid solar and battery farm connected to a distribution station. The objective is to achieve the maximum daily peak load reduction and charge battery with maximum solar photovoltaic energy. The innovative part of the paper lies in the treatment for the errors in solar and demand forecasts to then optimize the battery scheduler. To test the effectiveness of the proposed methodology, it was applied in the data science challenge Presumed Open Data 2021. With the historical Numerical Weather Prediction (NWP) data, solar power plant generation and distribution-level demand data provided, the proposed methodology was tested for four different seasons. The evaluation metric used is the peak reduction score (defined in the paper), and our approach has improved this KPI from 82.84 to 89.83. The solution developed achieved a final place of 5th (out of 55 teams) in the challenge.

show abstract

supporting

confidence: 77%

Section: Solar Forecasting Reviewmentioning

confidence: 99%

Optimized Charge Controller Schedule in Hybrid Solar-Battery Farms for Peak Load Reduction

Barta

Pasztor

Prava³

2021

Energies

View full text Add to dashboard Cite

show abstract

“…The output layer gives the output predictive result based on the analysis of the hidden layers. The contributions of the works [30–56] that propose ANN‐based SPF are presented in Table 2.…”

Section: Methodsmentioning

confidence: 99%

“…The forecasting horizon typically ranges from 1 h to 1 day [93]. Reviewed works [30, 32–36, 38, 39, 41–45, 47, 48, 59–63, 71, 75, 81, 82, 84] focus on short‐term SPF by using different models and methodologies.…”

Section: Methodologies and Data Analysismentioning

confidence: 99%

See 1 more Smart Citation

A taxonomy of short‐term solar power forecasting: Classifications focused on climatic conditions and input data

Bazionis,

Kousounadis‐Knousen,

Georgilakis

et al. 2023

IET Renewable Power Gen

View full text Add to dashboard Cite

A review of the state‐of‐the‐art in short‐term Solar Power Forecasting (SPF) methodologies is presented in this paper. Over the last few years, developing and improving solar forecasting models has been the main focus of researchers, considering the need to efficiently increase their forecasting accuracy. Forecasting models aim to be used as an efficient tool to help with the stability and control of energy systems and electricity markets. Intending to further comprehend the factors affecting the quality of SPF models, this paper focuses on short‐term solar forecasting methodologies since they pose a crucial role in the daily operation and scheduling of power systems, since they focus on forecasting horizons typically ranging from 1 h to 1 day. The reviewed works are classified according to the climatic conditions, technical characteristics, and the forecasting errors of the different methodologies, providing readers with information over various different cases of SPF. Considering the need to improve the SPF efficiency, such classifications allow for important comparative conclusions to be drawn, depending on the location of each case and the meteorological data available. Future directions in the field of short‐term solar power forecasting are proposed considering the increasing development of SPF models’ architecture and their field of focus.

show abstract

Benchmark of eight commercial solutions for deterministic intra-day solar forecast

Lehmann¹,

Koessler²,

Gomez³

et al. 2023

EPJ Photovolt.

View full text Add to dashboard Cite

Continuous increase of the production of photovoltaic energy requires precise forecasting to ensure grid stability. This paper presents a detailed benchmark of eight commercial forecasting solutions for intra-day solar forecasts. The comparison was carried out on a period of six months, from November to May 2021, on seven different PV plants located in different countries of the northern hemisphere. Performance evaluation metrics MAE, RMSE and MBE are used in order to analyze the forecasting precision. It is shown that forecasting solar power remains challenging, as shown by the important dispersion between the actors that we have observed.

show abstract

Intra-day Solar Irradiance Forecasting Based on Artificial Neural Networks

Cited by 7 publications

References 6 publications

Optimized Charge Controller Schedule in Hybrid Solar-Battery Farms for Peak Load Reduction

Optimized Charge Controller Schedule in Hybrid Solar-Battery Farms for Peak Load Reduction

A taxonomy of short‐term solar power forecasting: Classifications focused on climatic conditions and input data

Benchmark of eight commercial solutions for deterministic intra-day solar forecast

Contact Info

Product

Resources

About