A review and evaluation of solar forecasting technologies

Gupta, Anuj; Gupta, Kapil; Saroha, Sumit

doi:10.1016/j.matpr.2021.04.491

Cited by 15 publications

(13 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The temperature of the module and its level of efficiency depend on the ambient temperature and the amount of absorbed radiation, as well as the wind speed. When the ambient temperature increases (It is related to cloud cover), the module temperature rises and its efficiency also increases, while the wind speed reduces the module temperature and heat loss [ 33 ]. It is concluded that there was a weak correlation between atmospheric temperature and PV power output while there was an extremely weak correlation between wind speed and PV power output.…”

Section: Parameters Affecting Solar Power Forecastingmentioning

confidence: 99%

A Comprehensive Review on Ensemble Solar Power Forecasting Algorithms

Rahimi

Park

Choi

et al. 2023

J. Electr. Eng. Technol.

View full text Add to dashboard Cite

With increasing demand for energy, the penetration of alternative sources such as renewable energy in power grids has increased. Solar energy is one of the most common and well-known sources of energy in existing networks. But because of its non-stationary and non-linear characteristics, it needs to predict solar irradiance to provide more reliable Photovoltaic (PV) plants and manage the power of supply and demand. Although there are various methods to predict the solar irradiance. This paper gives the overview of recent studies with focus on solar irradiance forecasting with ensemble methods which are divided into two main categories: competitive and cooperative ensemble forecasting. In addition, parameter diversity and data diversity are considered as competitive ensemble forecasting and also preprocessing and post-processing are as cooperative ensemble forecasting. All these ensemble forecasting methods are investigated in this study. In the end, the conclusion has been drawn and the recommendations for future studies have been discussed.

show abstract

Section: Parameters Affecting Solar Power Forecastingmentioning

confidence: 99%

A Comprehensive Review on Ensemble Solar Power Forecasting Algorithms

Rahimi

Park

Choi

et al. 2023

J. Electr. Eng. Technol.

View full text Add to dashboard Cite

show abstract

“…. ŷn ) is the forecasted solar irradiance time series, used to calculating the performance of proposed model [2,3].…”

Section: Performance Criteriamentioning

confidence: 99%

“…The renewable energy sector in India, as an example of emerging countries, has grown at an exponential rate during the last two decades. India has even established a special ministry for RES; Ministry of New and Renewable Energy (MNRE), with a goal of generating 175 GW of energy from RES by the end of 2022; with 100 GW from solar alone [2,3]. Furthermore, according to several studies, the power grid will be completely functioning on the renewable energy source (RES) by the end of 2050 [4].…”

Section: Introductionmentioning

confidence: 99%

Short Term Solar Irradiation Prediction Framework Based on EEMD-GA-LSTM Method

Gupta

Saroha

2023

SPEE

View full text Add to dashboard Cite

Accurate short term solar irradiation forecasting is necessary for smart grid stability and to manage bilateral contract negotiations between suppliers and customers. Traditional machine learning methods are unable to acquire and rectify nonlinear characteristics from solar dataset, which not only complicates model construction but also affect prediction accuracy. To address these issues, a deep learning based architecture with predictive analysis strategy is developed in this manuscript. In the first stage, the original solar irradiation sequences are divided into many intrinsic mode functions to generate a prospective feature set using a sophisticated signal decomposition technique. After that, an iteration method is used to generate a prospective range of frequency related to deep learning model. This method is created by linked algorithm using the GA and deep learning network. The findings by the proposed model employing sequences obtained by the preprocessing methodology considerable improve prediction accuracy as comparison to conventional models. In contrast, when confronted with a high resolution dataset derived from big data set, the chosen dataset may not only conduct a huge data reduction, but also enhances forecasting accuracy up to 22.74 percent over a variety of evaluation metrics. As a result, the proposed method might be used to predict short-term solar irradiation with greater accuracy using a solar dataset.

show abstract

“…In [27], the authors provide a systematic analysis for techniques utilized in the literature review to predict solar irradiance. The work's target is to see how the input data of meteorology, time slots, optimization, sample size and preprocessing methodology affect the complexity of models and their accuracy.…”

Section: A Related Work Of Existing Survey Papersmentioning

confidence: 99%

A Survey of Preprocessing Methods Used for Analysis of Big Data Originated From Smart Grids

Alghamdi

Javaid

2022

IEEE Access

View full text Add to dashboard Cite

In this paper, a brief survey of data preprocessing methods is presented. Specifically, the data preprocessing methods used in the smart grid (SG) domain are surveyed. Also, with the advent of SG, data collection on a large scale became possible. The data is essential for electricity demand, generation and price forecasting, which plays an important role in making energy efficient decisions, and long and short term predictions regarding energy generation, consumption and storage. However, the forecasting accuracy decreases when data is used in raw form. Hence, data preprocessing is considered essential. This paper provides an overview of the data preprocessing methods and a detailed discussion of the methods used in the existing literature. A comparison of the methods is also given. A survey of closely related survey papers is also presented and the papers are compared based on their contributions. Moreover, based on the discussion of the data preprocessing methods, a narrative is built with a critical analysis. Finally, future research directions are discussed to guide the readers.

show abstract

A review and evaluation of solar forecasting technologies

Cited by 15 publications

References 29 publications

A Comprehensive Review on Ensemble Solar Power Forecasting Algorithms

A Comprehensive Review on Ensemble Solar Power Forecasting Algorithms

Short Term Solar Irradiation Prediction Framework Based on EEMD-GA-LSTM Method

A Survey of Preprocessing Methods Used for Analysis of Big Data Originated From Smart Grids

Contact Info

Product

Resources

About