A comprehensive review and analysis of solar forecasting techniques

Singla, Pradeep; Duhan, Manoj; Saroha, Sumit

doi:10.1007/s11708-021-0722-7

Cited by 78 publications

(38 citation statements)

References 150 publications

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“…( ) (11) Table II represents the different error measure applications by the various highly cited studies in the recent years to observe the performance of the SFM. The used measure is denoted by the symbol of tick against each paper.…”

Section: Standard Deviation Error (Sde)mentioning

confidence: 99%

“…It is due to the fact that the largest errors in the solar forecasting are highly undesirable. RMSE penalize the model performance for the highest errors present in the final results [11].…”

Section: A Key Findings: Deterministic Forecastingmentioning

confidence: 99%

“…MASE is used to overcome the problem of MAPE, MASE is considered to evaluate the performance of a SFM. It is another MAE but scaled by the MAE of benchmark/ reference model [11]. However, MAAPE; another version of MAPE is uncommon in the literature of solar forecasting but also used by some of the potential studies [70].…”

Section: A Key Findings: Deterministic Forecastingmentioning

confidence: 99%

“…Furthermore, it is popularly used in the case of time series-based forecasting where its emphasis is on the variability of forecasted values with respect to actual values. In other words, by using this error measure, the lesser variability in the forecast can be detected by obtaining the larger value of KSI [11].…”

Section: A Key Findings: Deterministic Forecastingmentioning

confidence: 99%

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Review of Different Error Metrics: A Case of Solar Forecasting

Singla¹,

Duhan

Saroha

2021

AJSE

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Renewable energy systems (RES) are no longer confined to being used as a stand-alone entity in the modern era. These RES, especially solar panels are also used with the grid power systems to supply electricity. However, precise forecasting of solar irradiance is necessary to ensure that the grid operates in a balanced and planned manner. Various solar forecasting models (SFM) are presented in the literature to produce an accurate solar forecast. Nevertheless, each model has gone through the step of evaluation of its accuracy using some error measures. Many error measures are discussed in the literature for deterministic as well as probabilistic solar forecasting. But, each study has its own selected error measure which sometimes landed on a wrong interpretation of results if not selected appropriately. As a result, this paper offers a critical assessment of several common error metrics with the goal of discussing alternative error metrics and establishing a viable set of error metrics for deterministic and probabilistic solar forecasting. Based on highly cited research from the last three years (2019-2021), error measures for both types of forecasting are presented with their basic functionalities, advantages & limitations which equipped the reader to pick the required compatible metrics

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Section: Standard Deviation Error (Sde)mentioning

confidence: 99%

Section: A Key Findings: Deterministic Forecastingmentioning

confidence: 99%

Section: A Key Findings: Deterministic Forecastingmentioning

confidence: 99%

Section: A Key Findings: Deterministic Forecastingmentioning

confidence: 99%

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Review of Different Error Metrics: A Case of Solar Forecasting

Singla¹,

Duhan

Saroha

2021

AJSE

Self Cite

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“…Regarding forecasting horizon, while various classifications can be found in the literature [10,36,47,52,53,67,68], this work considers the one proposed by Antonanzas et al [49] (found in Table 1) as it is easy to interpret (i.e., avoids abstract expressions, such as "short", "medium", "long-term", to which different authors often associate different horizon ranges) and ensures that shorter-term forecasting is differentiated from larger intra-day horizons.…”

mentioning

confidence: 99%

Review on Spatio-Temporal Solar Forecasting Methods Driven by In Situ Measurements or Their Combination with Satellite and Numerical Weather Prediction (NWP) Estimates

et al. 2022

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To better forecast solar variability, spatio-temporal methods exploit spatially distributed solar time series, seeking to improve forecasting accuracy by including neighboring solar information. This review work is, to the authors’ understanding, the first to offer a compendium of references published since 2011 on such approaches for global horizontal irradiance and photovoltaic generation. The identified bibliography was categorized according to different parameters (method, data sources, baselines, performance metrics, forecasting horizon), and associated statistics were explored. Lastly, general findings are outlined, and suggestions for future research are provided based on the identification of less explored methods and data sources.

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Embedding the weather prediction errors (WPE) into the photovoltaic (PV) forecasting method using deep learning

Bâra,

Oprea

2024

Journal of Forecasting

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The creation of features makes the difference in improving the photovoltaic forecast (PVF) for on‐grid, hybrid and off‐grid PV systems. The importance of the PVF is tremendous, and it can be essential in optimizing the home appliances to maximize the Renewable Energy Sources (RES) usage or to create performant bids for the electricity market. Several use cases are considered from the connectivity point of view. Therefore, in this paper, we propose a Weather Prediction Error (WPE)‐based method that uses a Stacking Regressor (SR) for various PV systems that coexist in the emerging Energy Communities (EC) landscape. The novelty of the research we conduct consists in proposing several features and determining the coefficients to adjust the PVF based on WPE. The forecast results of four types of PV systems from size and connectivity point of view are investigated. Compared with individual Machine Learning (ML) models, R2 increases with more than 3% with the SR and with more than 6% after applying the adjustment coefficients. Nevertheless, the major improvement is recorded for the off‐grid inverter and for the large industrial PV power plant, demonstrating that the proposed model is suitable for these types of systems. The other metrics improved as well, especially Mean Average Error (MAE) that decreases between 10% and 23%. A significant decrease is in the case of the industrial on‐grid PV, from 130 kW to 123 kW using the SR and to 107 kW after adjustments. This represents around 18% from the initial MAE. The ratio of daily deviations is also improved using the SR. For all the PV systems, the ratio stabilizes in a shorter interval, from daily values between 0.78 and 1.33 obtained with the ML models to values between 0.83 and 1.24 obtained with the SR. After the final adjustments, the interval becomes shorter, having daily values between 0.90 and 1.10.

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A comprehensive review and analysis of solar forecasting techniques

Cited by 78 publications

References 150 publications

Review of Different Error Metrics: A Case of Solar Forecasting

Review of Different Error Metrics: A Case of Solar Forecasting

Review on Spatio-Temporal Solar Forecasting Methods Driven by In Situ Measurements or Their Combination with Satellite and Numerical Weather Prediction (NWP) Estimates

Embedding the weather prediction errors (WPE) into the photovoltaic (PV) forecasting method using deep learning

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