Reconciling solar forecasts: Sequential reconciliation

Yagli, Gokhan Mert; Yang, Di; Srinivasan, Dipti

doi:10.1016/j.solener.2018.12.075

Cited by 42 publications

(18 citation statements)

References 10 publications

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“…The temporal reconciliation method has been found to outperform NWP forecasts generated through 3TIER, with a FS up to 0.3. The authors in [104] proposed 'sequential reconciliation' by combining power system spatial and temporal hierarchies so that aggregate-consistent forecasts are generated both in spatial and temporal domains. Independently generated base forecasts are reconciled using two techniques: (i) forecasts are firstly reconciled in space then in time and (ii) forecasts are firstly reconciled in time then in space.…”

Section: General Descriptionmentioning

confidence: 99%

Solar irradiance resource and forecasting: a comprehensive review

Kumar

Yagli

Kashyap³

et al. 2020

IET Renewable Power Generation

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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: General Descriptionmentioning

confidence: 99%

Solar irradiance resource and forecasting: a comprehensive review

Kumar

Yagli

Kashyap³

et al. 2020

IET Renewable Power Generation

Self Cite

116

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“…A better solution would be to produce forecasts at all hierarchical levels and suitably combine them so that the forecasts across the various aggregation levels are coherent . Combining point forecasts across different cross-sectional [1, 2, 4, 11, 12], temporal [3, 13–19] as well as cross-temporal [20–22] aggregation levels has been extensively studied in the literature. More recently, studies have examined the case of probabilistic and density hierarchical forecasts as opposed to point forecasts [5, 23].…”

Section: Background Researchmentioning

confidence: 99%

Improving the forecasting performance of temporal hierarchies

2019

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Temporal hierarchies have been widely used during the past few years as they are capable to provide more accurate coherent forecasts at different planning horizons. However, they still display some limitations, being mainly subject to the forecasting methods used for generating the base forecasts and the particularities of the examined series. This paper deals with such limitations by considering three different strategies: (i) combining forecasts of multiple methods, (ii) applying bias adjustments and (iii) selectively implementing temporal hierarchies to avoid seasonal shrinkage. The proposed strategies can be applied either separately or simultaneously, being complements to the method considered for reconciling the base forecasts and completely independent from each other. Their effect is evaluated using the monthly series of the M and M3 competitions. The results are very promising, displaying lots of potential for improving the performance of temporal hierarchies, both in terms of accuracy and bias.

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“…The concept of reconciliation emerges when the forecast quantity, such as renewable power generation [17][18][19], electrical load [20], tourism demand [21], or FMCG demand [22], can be modeled as a hierarchy. Due to the different information sets available at various levels in the hierarchy, e.g., retailers' business strategy is opaque to the distributor, and modeling uncertainties, the lower-level forecasts almost surely do not sum up to the higher-level forecasts.…”

Section: On Forecast Reconciliationmentioning

confidence: 99%

Impact of Information Sharing and Forecast Combination on Fast-Moving-Consumer-Goods Demand Forecast Accuracy

Yang

Zhang

2019

Information

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This article empirically demonstrates the impacts of truthfully sharing forecast information and using forecast combinations in a fast-moving-consumer-goods (FMCG) supply chain. Although it is known a priori that sharing information improves the overall efficiency of a supply chain, information such as pricing or promotional strategy is often kept proprietary for competitive reasons. In this regard, it is herein shown that simply sharing the retail-level forecasts—this does not reveal the exact business strategy, due to the effect of omni-channel sales—yields nearly all the benefits of sharing all pertinent information that influences FMCG demand. In addition, various forecast combination methods are used to further stabilize the forecasts, in situations where multiple forecasting models are used during operation. In other words, it is shown that combining forecasts is less risky than “betting” on any component model.

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Reconciling solar forecasts: Sequential reconciliation

Cited by 42 publications

References 10 publications

Solar irradiance resource and forecasting: a comprehensive review

Solar irradiance resource and forecasting: a comprehensive review

Improving the forecasting performance of temporal hierarchies

Impact of Information Sharing and Forecast Combination on Fast-Moving-Consumer-Goods Demand Forecast Accuracy

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