Weather forecasting error in solar energy forecasting

Sangrody, Hossein; Sarailoo, Morteza; Zhou, N.; Tran, Nhu; Motalleb, Mahdi; Foruzan, Elham

doi:10.1049/iet-rpg.2016.1043

Cited by 53 publications

(31 citation statements)

References 23 publications

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“…The number of neurons in input and output layers are respectively the same as the number of predictors and targets; however, the number of hidden layers and their neurons are specified by user. For many cases, one or two hidden layers provide fairly good results [20].…”

Section: Ann Methodsmentioning

confidence: 99%

On the performance of forecasting models in the presence of input uncertainty

Sangrody

Sarailoo

Zhou

et al. 2017

2017 North American Power Symposium (NAPS)

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Abstract-Nowadays, w ith the unprecedented penetration of renewable distributed energy resources (DERs), the necessity of an efficient energy forecasting model is more demanding than before. Generally, forecasting models are trained using observed weather data while the trained models are applied for energy forecasting using forecasted weather data. In this study, the performance of several commonly used forecasting methods in the presence of weather predictors with uncertainty is assessed and compared. Accordingly, both observed and forecasted weather data are collected, then the influential predictors for solar PV generation forecasting model are selected using several measures. Using observed and forecasted weather data, an analysis on the uncertainty of weather variables is represented by MAE and bootstrapping. The energy forecasting model is trained using observed weather data, and finally, the performance of several commonly used forecasting methods in solar energy forecasting is simulated and compared for a real case study.

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Section: Ann Methodsmentioning

confidence: 99%

On the performance of forecasting models in the presence of input uncertainty

Sangrody

Sarailoo

Zhou

et al. 2017

2017 North American Power Symposium (NAPS)

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“…[2,3]. However, it is not possible to accurately forecast the variables and thus there exist many uncertainties (e.g., demand power [4][5][6], renewable energy generation [7][8][9][10][11][12][13], grid blackouts [14,15], plug-in electric vehicles [16,17], etc.) during power system operations.…”

Section: Introductionmentioning

confidence: 99%

Chance Constrained Optimal Power Flow Using the Inner-Outer Approximation Approach

Mohagheghi

Geletu

Bremser

et al. 2018

2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Syst

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In recent years, there has been a huge trend to penetrate renewable energy sources into energy networks. However, these sources introduce uncertain power generation depending on environmental conditions. Therefore, finding 'optimal' and 'feasible' operation strategies is still a big challenge for network operators and thus, an appropriate optimization approach is of utmost importance. In this paper, we formulate the optimal power flow (OPF) with uncertainties as a chance constrained optimization problem. Since uncertainties in the network are usually 'non-Gaussian' distributed random variables, the chance constraints cannot be directly converted to deterministic constraints. Therefore, in this paper we use the recently-developed approach of inner-outer approximation to approximately solve the chance constrained OPF. The effectiveness of the approach is shown using DC OPF incorporating uncertain non-Gaussian distributed wind power. Keywords-Optimal power flow (OPF); chance constrained programming; inner-outer approximation approach; uncertain wind power generation; non-Gaussian distribution. NOMENCLATURE , ijIndices for buses. BSusceptance. () IsPiecewise continuous function.f Objective function.

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“…Statistical studies on the database from historical weather data for forecasting in [29] to probabilistic methods for reliability assessment based on historical data in [30] and a data-driven analysis on capacitor bank operation in [31] show that statistics derived from realworld data are commonly used for modeling and validation in power systems. The above literature review on synthetic grid modeling suggests that there is a need for a comprehensive statistical study on realworld power systems branch electrical and nonelectrical parameters.…”

Section: Introductionmentioning

confidence: 99%

Interdependence of Transmission Branch Parameters on the Voltage Levels

Athari¹,

Wang²

2018

Proceedings of the 51st Hawaii International Conference on System Sciences

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Transformers and transmission lines are critical components of a grid network. This paper analyzes the statistical properties of the electrical parameters of transmission branches and especially examines their interdependence on the voltage levels. Some interesting findings include: (a) with appropriate conversion of MVA rating, a transformer's per unit reactance exhibits consistent statistical pattern independent of voltage levels and capacity; (b) the distributed reactance (ohms/km) of transmission lines also has some consistent patterns regardless of voltage levels; (c)other parameters such as the branch resistance, the MVA ratings, the transmission line length, etc, manifest strong interdependence on the voltage levels which can be approximated by a power function with different power constants. The results will be useful in both creation of synthetic power grid test cases and validation of existing grid models.

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Weather forecasting error in solar energy forecasting

Cited by 53 publications

References 23 publications

On the performance of forecasting models in the presence of input uncertainty

On the performance of forecasting models in the presence of input uncertainty

Chance Constrained Optimal Power Flow Using the Inner-Outer Approximation Approach

Interdependence of Transmission Branch Parameters on the Voltage Levels

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