Multivariate Spatio-temporal Solar Generation Forecasting: A Unified Approach to Deal With Communication Failure and Invisible Sites

Kapourchali, Mohammad Heidari; Sepehry, Mojtaba; Aravinthan, Visvakumar

doi:10.1109/jsyst.2018.2869825

Cited by 14 publications

(9 citation statements)

References 27 publications

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“…Table 2 compiles all the references identified in this category. It is observed that the most frequently used methods are Auto-Regressive models with eXogenous inputs (ARX) [39,57,61,[70][71][72][73], Vector Auto-Regressive model using eXogenous inputs (VARX) [74], Kriging [59,[75][76][77], and Least Absolute Shrinkage and Selection Operator (LASSO) [14,17,30,78,79].…”

Section: Traditional Statistical Methodsmentioning

confidence: 99%

“…Kriging is an interpolation method capable of forecasting for unsampled sites [75], where its best-known variants are simple, ordinary, and universal kriging. Other variations have been proposed, such as in Heidari Kapourchali et al [77], where a multivariate extension designated as co-kriging is proposed, or Jamaly and Kleissl [76], where isotropic and anisotropic spatial kriging are compared.…”

Section: Traditional Statistical Methodsmentioning

confidence: 99%

“…From the small sample on probabilistic forecasting (10 publications), was observed that the only frequent metric is the Continuous Ranked Probability Score (CRPS), while others such as reliability, sharpness, entropy, or Pinball Loss only appear once. However, it is interesting to note that some of these probabilistic publications actually end up conducting a deterministic assessment of a predefined quantile [77,125,126]. For a more detailed description of most of these metrics, please refer to [2,130].…”

Section: Evaluation Metricsmentioning

confidence: 99%

See 2 more Smart Citations

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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Section: Traditional Statistical Methodsmentioning

confidence: 99%

Section: Traditional Statistical Methodsmentioning

confidence: 99%

Section: Evaluation Metricsmentioning

confidence: 99%

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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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“…For instance, a novel machine learning tool, support vector machine, can be applied for time-series prediction in the power system [7]. Besides, a low-rank tensor learning model can be utilized to predict system measurements such as solar power generation [8]. Last but not least, the use of decision trees can handle a large amount of wide-area information to keep the stability of the power system [9].…”

Section: Introductionmentioning

confidence: 99%

Bayesian Deep Learning for Dynamic Power System State Prediction Considering Renewable Energy Uncertainty

Zhang

2022

Journal of Modern Power Systems and Clean Energy

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Modern power systems are incorporated with distributed energy sources to be environmental-friendly and costeffective. However, due to the uncertainties of the system integrated with renewable energy sources, effective strategies need to be adopted to stabilize the entire power systems. Hence, the system operators need accurate prediction tools to forecast the dynamic system states effectively. In this paper, we propose a Bayesian deep learning approach to predict the dynamic system state in a general power system. First, the input system dataset with multiple system features requires the data pre-processing stage. Second, we obtain the dynamic state matrix of a general power system through the Newton-Raphson power flow model. Third, by incorporating the state matrix with the system features, we propose a Bayesian long short-term memory (BLSTM) network to predict the dynamic system state variables accurately. Simulation results show that the accurate prediction can be achieved at different scales of power systems through the proposed Bayesian deep learning approach.

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“…They propose to fill the missing gaps by finding a representative PV signal from the same geographical area and replacing the missing segment by the representative signal. The work in [26] proposed a low-rank tensor learning scheme to reconstruct the missing gaps. The low-rank tensor model implicitly considers the spatio-temporal correlations of multi-site PV production signals.…”

Section: Introductionmentioning

confidence: 99%

High-Resolution PV Forecasting from Imperfect Data: A Graph-Based Solution

et al. 2020

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Operating power systems with large amounts of renewables requires predicting future photovoltaic (PV) production with fine temporal and spatial resolution. State-of-the-art techniques combine numerical weather predictions with statistical post-processing, but their resolution is too coarse for applications such as local congestion management. In this paper we introduce computing methods for multi-site PV forecasting, which exploit the intuition that PV systems provide a dense network of simple weather stations. These methods rely entirely on production data and address the real-life challenges that come with them, such as noise and gaps. Our approach builds on graph signal processing for signal reconstruction and for forecasting with a linear, spatio-temporal autoregressive (ST-AR) model. It also introduces a data-driven clear-sky production estimation for normalization. The proposed framework was evaluated over one year on both 303 real PV systems under commercial monitoring across Switzerland, and 1000 simulated ones based on high-resolution weather data. The results demonstrate the performance and robustness of the approach: with gaps of four hours on average in the input data, the average daytime NRMSE over a six-hour forecasting horizon (in 15 min steps) and over all systems is 13.8% and 9% for the real and synthetic data sets, respectively.

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Multivariate Spatio-temporal Solar Generation Forecasting: A Unified Approach to Deal With Communication Failure and Invisible Sites

Cited by 14 publications

References 27 publications

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

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

Bayesian Deep Learning for Dynamic Power System State Prediction Considering Renewable Energy Uncertainty

High-Resolution PV Forecasting from Imperfect Data: A Graph-Based Solution

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