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

Sangrody, Hossein; Sarailoo, Morteza; Zhou, N.; Shokrollahi, Ahmad; Foruzan, Elham

doi:10.1109/naps.2017.8107379

Cited by 12 publications

(7 citation statements)

References 22 publications

(36 reference statements)

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“…Among the weather variables provided by Weather station no. 1, temperature, dew point, humidity, and wind speed are relevant for solar energy forecasting [34]. Using the weather data at station no.…”

Section: Numerical Resultsmentioning

confidence: 99%

Similarity-Based Models for Day-Ahead Solar PV Generation Forecasting

2020

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Accurate forecasting of solar photovoltaic (PV) power for the next day plays an important role in unit commitment, economic dispatch, and storage system management. However, forecasting solar PV power in high temporal resolution such as five-minute resolution is challenging because most of PV forecasting models can only achieve the same temporal resolution as their predictors(i.e., weather variables), whose temporal resolution is usually low (i.e., hourly). To address this challenge, similaritybased forecasting models (SBFMs) are advocated in this paper to forecast PV power in high temporal resolution using low temporal resolution weather variables. To effectively generalize the model for different scenarios of available weather data, three forecasting models (i.e., basic SBFM, categorical SBFM, and hierarchical SBFM) are proposed. As a case study, the PV power generated by the solar panels on the rooftop of a commercial building is forecasted for the next day with a five-minute resolution under three different scenarios of available weather data. The leave-one-out cross-validation analysis reveals that using only two or three weather variables, the proposed SBFMs can achieve higher forecasting accuracy than several benchmark models.

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Section: Numerical Resultsmentioning

confidence: 99%

Similarity-Based Models for Day-Ahead Solar PV Generation Forecasting

2020

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“…Using feature selection method, 10 features related to past loads are selected i.e., 1,2,3,4,6,7,8,14,26 and 28 days before the forecasting day. In addition, some other features such as holidays, day type of a week, and month of a year have been taken into account in inputs by binary features.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…By considering influential parameters of load forecasting in different time horizons and uncertainty in the inputs of forecasting models, it can be deduced that the longer time horizon is, the more challenge the load forecasting will be to get accurate point estimation of future demand. In this regards, most of studies in load forecasting era have focused on very short-term and short-term load forecasting (VSTLF and STLF), in which the forecasting models get benefits of accurate predictors like accurate forecasted weather variables as well as influential lag values of historical load data [1]. Thus, the forecasting results in VSTLF and STLF have usually led to fairly accurate results.…”

Section: Introductionmentioning

confidence: 99%

Medium-Term Load Forecasting Using Support Vector Regression, Feature Selection, and Symbiotic Organism Search Optimization

Zare-Noghabi

Shabanzadeh

Sangrody

2019

2019 IEEE Power &Amp; Energy Society General Meeting (PESGM)

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An accurate load forecasting has always been one of the main indispensable parts in the operation and planning of power systems. Among different time horizons of forecasting, while short-term load forecasting (STLF) and long-term load forecasting (LTLF) have respectively got benefits of accurate predictors and probabilistic forecasting, medium-term load forecasting (MTLF) requires more attentions due to its vital role in power system operation and planning such as optimal scheduling of generation units, robust planning programs for customer service, and economic supply. In this study, a hybrid method, composed of Support Vector Regression (SVR) and Symbiotic Organism Search Optimization (SOSO) method, is proposed for MTLF. In the proposed forecasting model, SVR is the main part of the forecasting algorithm while SOSO is embedded into it to optimize the parameters of SVR. In addition, a minimum redundancy-maximum relevance feature selection algorithm is applied in the preprocessing of input data. The proposed method is tested on EUNITE competition dataset and compared with some previous works to demonstrate its high performance.

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“…However, it is impossible to precisely forecast the values of these variables. In addition, there exist many other uncertainties (e.g., REG [38,[124][125][126][127][128], demand power [35,122,129,130], line outage [131][132][133], generator outage [131], plug-in electric vehicles [134,135], fuel price [131,136,137], and grid blackouts [138][139][140][141]) in the operation of energy networks. This poses numerous challenges for network operators when ensuring the reliability of the optimal operation strategies.…”

Section: Stochastic Emssmentioning

confidence: 99%

A Survey of Real-Time Optimal Power Flow

et al. 2018

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There has been a strong increase of penetration of renewable energies into power systems. However, the renewables pose new challenges for the operation of the networks. Particularly, wind power is intermittently fluctuating, and, therefore, the network operator has to fast update the operations correspondingly. This task should be performed by an online optimization. Therefore, real-time optimal power flow (RT-OPF) has become an attractive topic in recent years. This paper presents an overview of recent studies on RT-OPF under wind energy penetration, offering a critical review of the major advancements in RT-OPF. It describes the challenges in the realization of the RT-OPF and presents available approaches to address these challenges. The paper focuses on a number of topics which are reviewed in chronological order of appearance: offline energy management systems (EMSs) (deterministic and stochastic approaches) and real-time EMSs (constraint satisfaction-based and OPF-based methods). The particular challenges associated with the incorporation of battery storage systems in the networks are explored, and it is concluded that the current research on RT-OPF is not sufficient, and new solution approaches are needed.Energies 2018, 11, 3142 2 of 20 time. However, the incorporation of BSSs into the network leads to dynamic state operations and thus introduces dynamic model equations to the OPF, which makes the problem more complex to solve. This complexity is due to the fact that the dynamic optimization is described by differential/difference equations, whereas static optimization requires only algebraic equations [30].Another issue is that the REG rate is uncertain. It means their output cannot be forecasted accurately, and there may be discrepancies between the forecasted and actually realized values. The uncertainties can lead to constraint violations and thus safety problems if they are not handled properly. Therefore, deterministic OPF methods are not suitable for the real operation of the networks, or their solutions need further modifications before realization. Besides, owing to fast fluctuating REG, in particular wind power, the network operators need to fast update-i.e., in real-time-the operation strategies correspondingly, in order for the network to operate economically and in its feasible region. Therefore, the real-time energy management systems (RT-EMSs) have attracted increasing interest from many researchers. In this paper, we classify the RT-EMSs into two main categories: (1) constraint satisfaction-based RT-EMSs, and (2) OPF-based RT-EMSs. The former can satisfy the technical constraints of the network in real time, but the solutions may not be optimal. The latter provides optimal solutions in real time while satisfying the constraints. It is noted that the technical constraints could be different for these two categories, depending on the components of the network (details can be seen in Section 2).The remainder of the paper is organized as follows: Section 2 provides the general formulation of OPF...

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

Cited by 12 publications

References 22 publications

Similarity-Based Models for Day-Ahead Solar PV Generation Forecasting

Similarity-Based Models for Day-Ahead Solar PV Generation Forecasting

Medium-Term Load Forecasting Using Support Vector Regression, Feature Selection, and Symbiotic Organism Search Optimization

A Survey of Real-Time Optimal Power Flow

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