scholarly journals Generation Data of Synthetic High Frequency Solar Irradiance for Data-Driven Decision-Making in Electrical Distribution Grids

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4734
Author(s):  
Mohammad Rayati ◽  
Pasquale De Falco ◽  
Daniela Proto ◽  
Mokhtar Bozorg ◽  
Mauro Carpita
Keyword(s):  
Decision Making ◽  
Solar Irradiance ◽  
High Frequency ◽  
Power Production ◽  
Data Driven ◽  
Pv System ◽  
Clear Sky ◽  
Proposed Model ◽  
Irradiance Data ◽  
Distribution Grids

In this paper, we introduce a model representing the key characteristics of high frequency variations of solar irradiance and photovoltaic (PV) power production based on Clear Sky Index (CSI) data. The model is suitable for data-driven decision-making in electrical distribution grids, e.g., descriptive/predictive analyses, optimization, and numerical simulation. We concentrate on solar irradiance data since the power production of a PV system strongly correlates with solar irradiance at the site location. The solar irradiance is not constant due to the Earth’s orbit and irradiance absorption/scattering from the clouds. To simulate the operation of a PV system with one-minute resolution for a specific coordinate, we have to use a model based on the CSI of the solar irradiance data, capturing the uncertainties caused by cloud movements. The proposed model is based on clustering the days of each year into groups of days, e.g., (i) cloudy, (ii) intermittent cloudy, and (iii) clear sky. The CSI data of each group are divided into bins of magnitudes and the transition probabilities among the bins are identified to deliver a Markov Chain (MC) model to track the intraday weather condition variations. The proposed model is tested on the measurements of two PV systems located at two different climatic regions: (a) Yverdon-les-Bains, Switzerland; and (b) Oahu, Hawaii, USA. The model is compared with a previously published N-state MC model and the performance of the proposed model is elaborated.

scholarly journals COVID-19 Risk Minimization Decision Making Strategy Using Data-Driven Model

2021 ◽  
Vol 3 (1) ◽  
pp. 57-66
Author(s):  
Akey Sungheetha
Keyword(s):  
Decision Making ◽  
Data Driven ◽  
Risk Minimization ◽  
Proposed Model ◽  
Epidemic Intelligence ◽  
Spread Dynamics ◽  
Using Data ◽  
The Impact ◽  
Global Strategies

In order to establish social resilient and sustainable cities during the pandemic outbreak, it is essential to forecast the epidemic trends and trace infection by means of data-driven solution addressing the requirements of local operational defense applications and global strategies. The smartphone based Digital Proximity Tracing Technology (DPTT) has obtained a great deal of interest with the ongoing COVID-19 pandemic in terms of mitigation, containing and monitoring with the population acceptance insights and effectiveness of the function. The DPTTs and Data-Driven Epidemic Intelligence Strategies (DDEIS) are compared in this paper to identify the shortcomings and propose a novel solution to overcome them. In terms of epidemic resurgence risk minimization, guaranteeing public health safety and quick return of cities to normalcy, a social as well as technological solution may be provided by incorporating the key features of DDEIS. The role of human behavior is taken into consideration while assessing its limitations and benefits for policy making as well as individual decision making. The epidemiological model of SEIR (Susceptible–Exposed–Infectious–Recovered) provides preliminary data for the preferences of users in a DPTT. The impact of the proposed model on the spread dynamics of Covid-19 is evaluated and the results are presented.

scholarly journals A New Empirical Approach for Estimating Solar Insolation Using Air Temperature in Tropical and Mountainous Environments

2021 ◽  
Vol 11 (23) ◽  
pp. 11491
Author(s):  
Laura Sofía Hoyos-Gomez ◽  
Belizza Janet Ruiz-Mendoza
Keyword(s):  
Solar Irradiance ◽  
Statistical Error ◽  
Data Imputation ◽  
Imputation Model ◽  
Computational Costs ◽  
The Pacific ◽  
Proposed Model ◽  
Irradiance Data ◽  
Economic Indices ◽  
Better Than

Solar irradiance is an available resource that could support electrification in regions that are low on socio-economic indices. Therefore, it is increasingly important to understand the behavior of solar irradiance. and data on solar irradiance. Some locations, especially those with a low socio-economic population, do not have measured solar irradiance data, and if such information exists, it is not complete. There are different approaches for estimating solar irradiance, from learning models to empirical models. The latter has the advantage of low computational costs, allowing its wide use. Researchers estimate solar energy resources using information from other meteorological variables, such as temperature. However, there is no broad analysis of these techniques in tropical and mountainous environments. Therefore, in order to address this gap, our research analyzes the performance of three well-known empirical temperature-based models—Hargreaves and Samani, Bristol and Campbell, and Okundamiya and Nzeako—and proposes a new one for tropical and mountainous environments. The new empirical technique models daily solar irradiance in some areas better than the other three models. Statistical error comparison allows us to select the best model for each location and determines the data imputation model. Hargreaves and Samani’s model had better results in the Pacific zone with an average RMSE of 936,195 Wh/m2 day, SD of 36,01%, MAE of 748,435 Wh/m2 day, and U95 of 1.836,325 Wh/m2 day. The new proposed model showed better results in the Andean and Amazon zones with an average RMSE of 1.032,99 Wh/m2 day, SD of 34,455 Wh/m2 day, MAE of 825,46 Wh/m2 day, and U95 of 2.025,84 Wh/m2 day. Another result was the linear relationship between the new empirical model constants and the altitude of 2500 MASL (mean above sea level).

scholarly journals Analytical Modelling and Simulation of Photovoltaic Panels and Arrays

2007 ◽  
Vol 4 (1) ◽  
pp. 75 ◽  
Author(s):  
H. Bourdoucen ◽  
A. Gastli
Keyword(s):  
Mathematical Modelling ◽  
Analytical Model ◽  
Solar Irradiance ◽  
Physical Parameters ◽  
System Operation ◽  
Pv System ◽  
Photovoltaic Panels ◽  
Proposed Model ◽  
Panel Effects ◽  
Total Agreement

In this paper, an analytical model for PV panels and arrays based on extracted physical parameters of solar cells is developed. The proposed model has the advantage of simplifying mathematical modelling for different configurations of cells and panels without losing efficiency of PV system operation. The effects of external parameters, mainly temperature and solar irradiance have been considered in the modelling. Due to their critical effects on the operation of the panel, effects of series and shunt resistances were also studied. The developed analytical model has been easily implemented, simulated and validated using both Spice and Matlab packages for different series and parallel configurations of cells and panels. The results obtained with these two programs are in total agreement, which make the proposed model very useful for researchers and designers for quick and accurate sizing of PV panels and arrays. 

Decision making based on intuitionistic fuzzy preference relations with additive approximate consistency

2020 ◽  
Vol 39 (3) ◽  
pp. 4041-4058
Author(s):  
Fang Liu ◽  
Xu Tan ◽  
Hui Yang ◽  
Hui Zhao
Keyword(s):  
Decision Making ◽  
Decision Makers ◽  
Real Interval ◽  
Preference Relations ◽  
Intuitionistic Fuzzy ◽  
Proposed Model ◽  
Fuzzy Preference Relations ◽  
Novel Concept ◽  
Fuzzy Weights ◽  
Fuzzy Preference

Intuitionistic fuzzy preference relations (IFPRs) have the natural ability to reflect the positive, the negative and the non-determinative judgements of decision makers. A decision making model is proposed by considering the inherent property of IFPRs in this study, where the main novelty comes with the introduction of the concept of additive approximate consistency. First, the consistency definitions of IFPRs are reviewed and the underlying ideas are analyzed. Second, by considering the allocation of the non-determinacy degree of decision makers’ opinions, the novel concept of approximate consistency for IFPRs is proposed. Then the additive approximate consistency of IFPRs is defined and the properties are studied. Third, the priorities of alternatives are derived from IFPRs with additive approximate consistency by considering the effects of the permutations of alternatives and the allocation of the non-determinacy degree. The rankings of alternatives based on real, interval and intuitionistic fuzzy weights are investigated, respectively. Finally, some comparisons are reported by carrying out numerical examples to show the novelty and advantage of the proposed model. It is found that the proposed model can offer various decision schemes due to the allocation of the non-determinacy degree of IFPRs.

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