A minutely solar irradiance forecasting method based on real-time sky image-irradiance mapping model

Wang, Fei; Xuan, Zhiming; Zhen, Zhao; Liu, Yu; Li, Kangping; Zhao, Liqiang; Shafie‐khah, Miadreza; Catalão, João P.S.

doi:10.1016/j.enconman.2020.113075

Cited by 110 publications

(29 citation statements)

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“…In comparison, the method introduced in this paper achieved an average RMSE of 108 Wm -2 , as shown in Table 5. Therefore, the forecasting model introduced in this paper performs better than [33].…”

Section: 32mentioning

confidence: 92%

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Sky Image-Based Localized, Short-Term Solar Irradiance Forecasting for Multiple PV Sites via Cloud Motion Tracking

Dissawa

Godaliyadda

Ekanayake

et al. 2021

International Journal of Photoenergy

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Power generation through solar photovoltaics has shown significant growth in recent years. However, high penetration of solar PV creates power system operational issues as a result of solar PV variability and uncertainty. Short-term PV variability mainly occurs due to the intermittency of cloud cover. Therefore, to mitigate the effects of PV variability, a sky-image-based, localized, global horizontal irradiance forecasting model was introduced considering the individual cloud motion, cloud thicknesses, and the elevations of clouds above the ground level. The proposed forecasting model works independently of any historical irradiance measurements. Two inexpensive sky camera systems were developed and placed in two different locations to obtain sky images for cloud tracking and cloud-based heights. Then, irradiance values for onsite and for a PV site located with a distance of 2 km from the main camera were forecasted for 1 minute, 5 minutes, and 15 minutes ahead of real-time. Results show that the three-level cloud categorization and the individual cloud movement tracking method introduced in this paper increase the forecasting accuracy. For partially cloudy and sunny days, the forecasting model for 15 min forecasting time interval achieved a positive skill factor concerning the persistent model. The accuracy of determining the correct irradiance state for a 1 min forecasting time interval using the proposed model is 81%. The average measures of RMSE, MAE, and SF obtained using the proposed method for 15 min forecasting time horizon are 101 Wm-2, 64 Wm-2, and 0.26, respectively. These forecasting accuracy levels are much higher than the other benchmarks considered in this paper.

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Section: 32mentioning

confidence: 92%

“…Reference [33] introduces a solar irradiance forecasting model based on surface irradiance mapping. In that model, initially, the mapping relationship between the information of the cloud pixels and irradiance was established, and then a sky image-irradiance mapping model is developed.…”

Section: 32mentioning

confidence: 99%

Sky Image-Based Localized, Short-Term Solar Irradiance Forecasting for Multiple PV Sites via Cloud Motion Tracking

Dissawa

Godaliyadda

Ekanayake

et al. 2021

International Journal of Photoenergy

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“…Nowadays, machine learning (ML) is perhaps the most popular approach in solar forecasting and load forecasting [2]. Although artificial neural networks (ANNs) and support vector machines (SVMS) are still the basis of machine learning methods in solar irradiance prediction, many other approaches have been used recently, such as k-nearest neighbors (kNN), random forest (RF), gradient boosted regression (GBR), hidden Markov models (HMMs), fuzzy logic (FL), wavelet networks (WNN), and long short-term memory networks (LSTM) [14][15][16][17][18][19][20][21][22]. Meanwhile, some hybrid algorithms are used to improve the prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Hour-Ahead Solar Irradiance Forecasting Models Based on LSTM Network

Huang

Zhang

et al. 2020

Mathematical Problems in Engineering

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The intermittence and fluctuation character of solar irradiance places severe limitations on most of its applications. The precise forecast of solar irradiance is the critical factor in predicting the output power of a photovoltaic power generation system. In the present study, Model I-A and Model II-B based on traditional long short-term memory (LSTM) are discussed, and the effects of different parameters are investigated; meanwhile, Model II-AC, Model II-AD, Model II-BC, and Model II-BD based on a novel LSTM-MLP structure with two-branch input are proposed for hour-ahead solar irradiance prediction. Different lagging time parameters and different main input and auxiliary input parameters have been discussed and analyzed. The proposed method is verified on real data over 5 years. The experimental results demonstrate that Model II-BD shows the best performance because it considers the weather information of the next moment, the root mean square error (RMSE) is 62.1618 W/m2, the normalized root mean square error (nRMSE) is 32.2702%, and the forecast skill (FS) is 0.4477. The proposed algorithm is 19.19% more accurate than the backpropagation neural network (BPNN) in terms of RMSE.

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“…Authors in (Chakraborty et al, 2019) addressed the problem of scheduling thermostatic devices with the objective to minimize power fluctuation and maximize users' thermal comfort. Reference (Lu et al, 2020) investigated the fundamentals and business mechanisms of resource aggregators in the electricity market. Authors presented a comprehensive overview of the current state of the art mechanisms and an analysis of the business strategies for resource aggregators.…”

Section: Introductionmentioning

confidence: 99%

High Level Controller-Based Energy Management for a Smart Building Integrated Microgrid With Electric Vehicle

2020

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This paper presents an effective approach for the modeling and optimization of a smart building integrated microgrid (BIM). The main objective is the development of a smart building energy management system (BEMS) which is in charge of optimally controlling the operation of a building-integrated-microgrid. More specifically, we consider a BIM consisting of a roof-top solar photovoltaic, wind turbine, energy storage unit, electric vehicle, micro-CHP, metering infrastructure, and loads. The BIM loads are used to satisfy the electric needs of occupants, while operating flexibility of thermal loads, which include building heating and hot water demand, are also considered. Emphasis is given to the optimal performance of the BIM, where the objective is to ensure the power balance between production and loads in the microgrid (electric and thermal). A centralized algorithm is implemented for the power management of all components as well as power exchanges with the electrical grid. The developed algorithm is tested through a case study, where the effects of both loads and renewable resources' variabilities on the operation of the BIM are analyzed via numerical simulations.

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A minutely solar irradiance forecasting method based on real-time sky image-irradiance mapping model

Cited by 110 publications

References 50 publications

Sky Image-Based Localized, Short-Term Solar Irradiance Forecasting for Multiple PV Sites via Cloud Motion Tracking

Sky Image-Based Localized, Short-Term Solar Irradiance Forecasting for Multiple PV Sites via Cloud Motion Tracking

A Comparison of Hour-Ahead Solar Irradiance Forecasting Models Based on LSTM Network

High Level Controller-Based Energy Management for a Smart Building Integrated Microgrid With Electric Vehicle

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