Intraday Load Forecasts with Uncertainty

Kozak, David; Holladay, Scott; Fasshauer, Gregory E.

doi:10.3390/en12101833

Cited by 5 publications

(9 citation statements)

References 37 publications

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“…Our target in this step is forecasting load data based on posterior process. It is assumed that the output function for a set of input amounts has a prior distribution as follows (Kozak et al, 2019):…”

Section: Prediction Of the System Load By Employing Posterior Processmentioning

confidence: 99%

Coordination of similar days and multi-objective posterior process to predict energy of transmission substation

Abdolrezaei

Siahkali

Olamaei

2021

JEDT

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Purpose This paper aims to present a hybrid model to mid-term forecast the load of transmission substations based on the knowledge of expert site and multi-objective posterior framework. The main important challenges in load forecasting are the different behavior of load in specific days. Regular days, holidays and special holidays, days after a holidays and days of load shifting are characterized by abnormal load profiles. The knowledge of these days is verified by expert operators in regional dispatching centers. Design/methodology/approach In this paper, a hybrid model for power prediction of transmission substations based on the combination of similar day selection and multi-objective posterior technique has been proposed. In the first step, the important data for prediction is provided. Posterior method is used in the second step for prediction that it is based on kernel functions. A multi-objective optimization has been formulated with three type of output accuracy measurement function that it is solved by non-dominated sorting genetic technique II (NSGT-II) method. TOPSIS way is used to find the best point of Pareto. Findings The presented method has been tested in four scenarios for three different transmission stations, and the test results have been compared. The presented results indicate that the presentation method has better results and is robust to different load characteristics, which can be used for better forecasting of different stations for better planning of repairs and network operation. Originality/value The main contributions of this paper can be categorized as follows: A hybrid model based on similar days selection and multi-objective framework posterior is presented. Similar day selection is done by expert site that the day type and days with scheduled repair are considered. Hyperparameters of posterior process are found by NSGT-II based on TOPSIS method.

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Section: Prediction Of the System Load By Employing Posterior Processmentioning

confidence: 99%

Coordination of similar days and multi-objective posterior process to predict energy of transmission substation

Abdolrezaei

Siahkali

Olamaei

2021

JEDT

View full text Add to dashboard Cite

show abstract

“…A prerequisite for developing an accurate forecasting model under atypical consumption behavior or power load uncertainty is a trigger that announces the decision factors for atypical consumption behavior to occur. This knowledge concerning the behav-2 of 15 ior of the load curve is determined by correlation between the influence factors, consumer data and statistical analysis of past consumption [3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

“…The daily forecast accuracy was also evaluated with MAPE and had better results with ARIMAX (5.5%) than with ANN (5.8%). Covering the largest US deregulated wholesale electricity market-Pennsylvania, New Jersey, Maryland (PJM)- [6] assessed forecasting under uncertainty in the pre-COVID-19 era by using a Gaussian process and obtaining an efficiency between 2.21% and 3.20% MAPE. Even though the atypical consumption was not related to COVID-19, the methodology used was suitable for any power load uncertainty related to an unforeseen event.…”

Section: Introductionmentioning

confidence: 99%

“…The modeled characteristics of the consumer to be analyzed [1] are an essential indicator of the health of the forecast, seen even more during unpredictable power load events, as the previous research states [3][4][5][6][7]. Power consumers absorbing load in similar socio-economic and weather/climate areas usually have similar consumer behavior, and consumption forecast models developed for a type of consumer can easily be adapted for forecasting the consumption of other consumers in the same conditions.…”

Section: Introductionmentioning

confidence: 99%

“…The advantages of this database are that it contains a huge number of consumers (households) and that the residential consumers are the ones that have the best correlation to the consumption influence factors, e.g., weather. Previous research was conducted [3][4][5][6][7] mainly on national or international databases containing all consumption, including residential, commercial, industrial, transport, etc. This means that the nonresidential consumers, which have the obligation to forecast their own consumption, accounted for more than half of the power consumption forecasted.…”

Section: Introductionmentioning

confidence: 99%

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Residential Short-Term Load Forecasting during Atypical Consumption Behavior

Hora¹,

Dan²,

Bendea³

et al. 2022

Energies

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Short-term load forecasting (STLF) is a fundamental tool for power networks’ proper functionality. As large consumers need to provide their own STLF, the residential consumers are the ones that need to be monitored and forecasted by the power network. There is a huge bibliography on all types of residential load forecast in which researchers have struggled to reach smaller forecasting errors. Regarding atypical consumption, we could see few titles before the coronavirus pandemic (COVID-19) restrictions, and afterwards all titles referred to the case of COVID-19. The purpose of this study was to identify, among the most used STLF methods—linear regression (LR), autoregressive integrated moving average (ARIMA) and artificial neural network (ANN)—the one that had the best response in atypical consumption behavior and to state the best action to be taken during atypical consumption behavior on the residential side. The original contribution of this paper regards the forecasting of loads that do not have reference historic data. As the most recent available scenario, we evaluated our forecast with respect to the database of consumption behavior altered by different COVID-19 pandemic restrictions and the cause and effect of the factors influencing residential consumption, both in urban and rural areas. To estimate and validate the results of the forecasts, multiyear hourly residential consumption databases were used. The main findings were related to the huge forecasting errors that were generated, three times higher, if the forecasting algorithm was not set up for atypical consumption. Among the forecasting algorithms deployed, the best results were generated by ANN, followed by ARIMA and LR. We concluded that the forecasting methods deployed retained their hierarchy and accuracy in forecasting error during atypical consumer behavior, similar to forecasting in normal conditions, if a trigger/alarm mechanism was in place and there was sufficient time to adapt/deploy the forecasting algorithm. All results are meant to be used as best practices during power load uncertainty and atypical consumption behavior.

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Performance Evaluation of Deregulated Nigerian Power System: A Case Study of Generation System

Jumba,

Haruna,

Aliyu

et al. 2023

Preprint

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In this work, comprehensive statistical data characterizing state of the Nigerian Electricity Generation System have been generated. Herein, Data Employment Analysis (DEA) scenario method performance analysis has been conducted on the generated data. The main outcome results showed that the installed and generation capacity increased by 23% and 44.3% for years 2016 and 2020, respectively. Similarly, the outcome of scenario analysis conducted using DEA on the constraints; show that, if the two major constraints can be eliminate i.e. gas and distribution the generation performance can be increases to approximately 99.9%. The market performance improved from 28.12% in 2016 to 40.83% in2020.

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Intraday Load Forecasts with Uncertainty

Cited by 5 publications

References 37 publications

Coordination of similar days and multi-objective posterior process to predict energy of transmission substation

Coordination of similar days and multi-objective posterior process to predict energy of transmission substation

Residential Short-Term Load Forecasting during Atypical Consumption Behavior

Performance Evaluation of Deregulated Nigerian Power System: A Case Study of Generation System

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