Sensor network based solar forecasting using a local vector autoregressive ridge framework

Xu, Jin; Yoo, Shinjae; Heiser, J.; Kalb, P.D.

doi:10.1145/2851613.2853124

Cited by 7 publications

(10 citation statements)

References 12 publications

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“…Satellite images predict the clouds movement over a broad region for a time period ranging from 0.5 h to 6 h. These images are mainly used for forecasting cloud movements of huge and slow‐moving clouds [28, 29, 68, 69]. Irradiance sensors are cost‐effective and easily employable when compared to the famous sky imaging devices [70]. Geographically distributed sensor networks with a dense arrangement of sensors and permanent measurement of output power are good enough to provide input data for short‐term CMVs with acceptable accuracy [4, 71].…”

Section: Methods For Solar Irradiance Forecastingmentioning

confidence: 99%

Solar irradiance resource and forecasting: a comprehensive review

Kumar

Yagli

Kashyap³

et al. 2020

IET Renewable Power Generation

116

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With the increase in demand for energy, penetration of alternative sources of energy in the power grid has increased. Photovoltaic (PV) energy is the most common and popular form of energy sources which is widely integrated into the existing grid. As solar energy is intermittent in nature, to ensure uninterrupted and reliable power supply to the prosumers, it is essential to forecast the solar irradiance. Accurate solar forecasting is necessary to facilitate large-scale modelling and deployment of PV plants without disrupting the quality and reliability of the power grid as well as to manage the power demand and supply. There are various methods to predict the solar irradiance such as numerical weather prediction methods, satellite-based approaches, cloud-image based methodologies, data-driven methods, and sensor-network based approaches. This study gives an overall review of the different resources and methods used for forecasting solar irradiance in different time horizons and also gives an extensive review of the sensor networks that are used for determining solar irradiance. The various error metrics and accessible data sets available for the sensor networks are also discussed that can be used for validation purposes.

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Section: Methods For Solar Irradiance Forecastingmentioning

confidence: 99%

Solar irradiance resource and forecasting: a comprehensive review

Kumar

Yagli

Kashyap³

et al. 2020

IET Renewable Power Generation

116

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“…While Sensor Networks often span over several hundred meters [19] or even kilometers [20], research on the use of low-cost illuminance meters showed that small networks can also detect cloud shadow movement when the sampling rate is high enough. Espinosa-Gavira et al [21] used a network of 16 lux meters, spanning only 15 m by 15 m, that was able to detect cloud shadow movement.…”

Section: Sensor Networkmentioning

confidence: 99%

A Review of Solar Forecasting Techniques and the Role of Artificial Intelligence

Barhmi,

Heynen,

Golroodbari

et al. 2024

Solar

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Solar energy forecasting is essential for the effective integration of solar power into electricity grids and the optimal management of renewable energy resources. Distinguishing itself from the existing literature, this review study provides a nuanced contribution by centering on advancements in forecasting techniques. While preceding reviews have examined factors such as meteorological input parameters, time horizons, the preprocessing methodology, optimization, and sample size, our study uniquely delves into a diverse spectrum of time horizons, spanning ultrashort intervals (1 min to 1 h) to more extended durations (up to 24 h). This temporal diversity equips decision makers in the renewable energy sector with tools for enhanced resource allocation and refined operational planning. Our investigation highlights the prominence of Artificial Intelligence (AI) techniques, specifically focusing on Neural Networks in solar energy forecasting, and we review supervised learning, regression, ensembles, and physics-based methods. This showcases a multifaceted approach to address the intricate challenges associated with solar energy predictions. The integration of Satellite Imagery, weather predictions, and historical data further augments precision in forecasting. In assessing forecasting models, our study describes various error metrics. While the existing literature discusses the importance of metrics, our emphasis lies on the significance of standardized datasets and benchmark methods to ensure accurate evaluations and facilitate meaningful comparisons with naive forecasts. This study stands as a significant advancement in the field, fostering the development of accurate models crucial for effective renewable energy planning and emphasizing the imperative for standardization, thus addressing key gaps in the existing research landscape.

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Section: Introductionmentioning

confidence: 99%

“…Most recently, several studies have proposed data-driven forecast methods that use multi-site spatio-temporal historical data for PV forecasting without requiring NWP or cloud movement information [11][12][13][14][15][16][17][18][19]. Simple and fast multi-site forecasting techniques using linear autoregressive (AR) models are proposed in [11][12][13]. The work in [12] proposes a lasso regularization in the AR model to automatically select the input variables, while the work in [13] further develops the AR model into a local vector AR model with ridge regularization that considers local weather changes.…”

Section: Introductionmentioning

confidence: 99%

“…Simple and fast multi-site forecasting techniques using linear autoregressive (AR) models are proposed in [11][12][13]. The work in [12] proposes a lasso regularization in the AR model to automatically select the input variables, while the work in [13] further develops the AR model into a local vector AR model with ridge regularization that considers local weather changes. However, nonlinear methods outperform the linear techniques for forecasting horizons longer than an hour ahead [20], and recent works have used advanced deep learning techniques to forecast multi-site PV power production for hourly horizons.…”

Section: Introductionmentioning

confidence: 99%

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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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Sensor network based solar forecasting using a local vector autoregressive ridge framework

Cited by 7 publications

References 12 publications

Solar irradiance resource and forecasting: a comprehensive review

Solar irradiance resource and forecasting: a comprehensive review

A Review of Solar Forecasting Techniques and the Role of Artificial Intelligence

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

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