Short-term aggregated load and distributed generation forecast using fuzzy grouping approach

Rejc, Matej; Einfalt, Alfred; Gawron-Deutsch, Tobias

doi:10.1109/sedst.2015.7315209

Cited by 5 publications

(3 citation statements)

References 9 publications

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“…[10] proposes a forecasting mechanism that generates a short-term load profile which is then used to feed an automatic frequency restoration reserve from renewable energy based on a virtual power plan. [14] Proposes a flexible model for predicting electric power required to be restored by distributed generation controls in an uncertain environment. [11] proposes a short-term load forecasting approach that can be used to model load demand forecasting for 20-minutes service restoration in secondary distribution power networks in Tanzania.…”

Section: Load Forecasting For Service Restorationmentioning

confidence: 99%

A Novel Load Forecasting Model for Automatic Fault Clearance in Secondary Distribution Electric Power Grid using an Extended-Multivariate Nonlinear Regression

Bakiri

Maziku²,

Mvungi³

et al. 2021

ijSmartGrid

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Smart grid is an emerging platform adopted by many electric power utility companies to enhance proper service delivery as well as cost-effective operations. Automatic Fault Detection and Clearance (AFDC) is a part of intelligent technology initiatives established on Tanzania's grid aiming at detecting, managing, and handling fault with little or without human intervention. Being one of the components of AFDC, the Load Forecasting (LF) plays important role in feeding restoration and distributed energy resource agencies. However, the efficiency of the existing LF approaches is found to be compromised when it comes to data whose distribution is characterized by a random-walk behavior. Therefore, this research work proposes an efficient AFDC-based LF (LF-AFDC) which is capable of generating load demand profile based on fault data. Firstly, the design requirements of the LF-AFDC framework are established using focus group discussion and literature survey. Secondly, the design of the LF model is achieved through parameter calibration from the existing Multivariate Non-Linear Regression (MNLR) method. Thirdly, the design of the LF-AFDC framework is achieved based on the design requirements for the realtime and fault-driven forecasting. Findings indicate the capability of the proposed framework to forecast the next load profile from the given fault-date, fault-time, restoration duration, Gross-Domestic Product (GDP), number of customers, and population information. Furthermore, the simulation results indicate the capability of the extended MNLR (e-MNLR) method to outperform the ANN, SVM, KNN, MICE, MissForest and MNLR models.

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Section: Load Forecasting For Service Restorationmentioning

confidence: 99%

A Novel Load Forecasting Model for Automatic Fault Clearance in Secondary Distribution Electric Power Grid using an Extended-Multivariate Nonlinear Regression

Bakiri

Maziku²,

Mvungi³

et al. 2021

ijSmartGrid

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show abstract

“…The analysis of the forecast errors are done by using the mean average percentage error (MAPE), because this measure is state of the literature and also used in like [8] or [10] for example. Equation (8) shows how the MAPE is calculated.…”

Section: Load Distribution By Voltage Measurementsmentioning

confidence: 99%

“…The already existing grid state forecasts from the literature, like they are presented in [5][6][7], are all constructed to handle many measurements from the grid and there is no solution to handle grids without measurements. Many load forecasts, like [8][9][10], need every load node to be measured to predict it. This is not realistic and will not happen next time in the low and medium voltage grids.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Different Development Possibilities of Distribution Grid State Forecasts

et al. 2020

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The number of renewable energy systems is still increasing. To reduce the worldwide CO2 emissions, there will be even more challenges in the distribution grids like currently upcoming charging stations or heat pumps. All these new electric systems in the low voltage (LV) and medium voltage (MV) levels are characterized by an unsteady behavior. To monitor and predict the behavior of these new flexible systems, a grid state forecast is needed. This software tool calculates wind, photovoltaic, and load forecasts. These power forecasts are already in the focus of research, but there are some specific use cases, which require a more specific solution. To get a variously applicable software tool, different new functions to improve an already existing grid state forecast tool were developed and evaluated. For example, it will be proofed if a grid state forecast tool can be improved by calculating the number or the base load of the loads in grid areas by just one available measurement. Another big subject exists in the exchange of forecast information between different voltage levels. How this can be realized and how big the effect on the forecast quality is, will be analyzed. The results of these evaluations will be shown in this paper.

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Building power demand forecasting using K-nearest neighbors model - initial approach

Valgaev

Kupzog

2016

2016 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC)

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Short-term aggregated load and distributed generation forecast using fuzzy grouping approach

Cited by 5 publications

References 9 publications

A Novel Load Forecasting Model for Automatic Fault Clearance in Secondary Distribution Electric Power Grid using an Extended-Multivariate Nonlinear Regression

A Novel Load Forecasting Model for Automatic Fault Clearance in Secondary Distribution Electric Power Grid using an Extended-Multivariate Nonlinear Regression

Evaluation of Different Development Possibilities of Distribution Grid State Forecasts

Building power demand forecasting using K-nearest neighbors model - initial approach

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