A novel method for decomposing electricity feeder load into elementary profiles from customer information

Gérossier, Alexis; Barbier, Thibaut; Girard, Robin

doi:10.1016/j.apenergy.2017.06.096

Cited by 9 publications

(7 citation statements)

References 21 publications

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“…Simulation results show that the proposed CLD-based load forecasting provides improved prediction accuracy compared to conventional schemes such as similarday-based load forecasting and hybrid approaches without decomposition. Furthermore, the proposed scheme outperforms the state-of-art method related to the decomposition of electricity feeders in terms of decomposition accuracy and forecasting result [10].…”

Section: Introductionmentioning

confidence: 91%

“…Then, a neural network is utilized as a load forecasting model [16]. Recently, a categorical load decomposition approach was proposed, where quadratic programming (QP) was employed to separate categorical profiles from various mixtures of customer load profiles [10], and Gaussian mixture model and hierarchical clustering were applied to identify the categorical building load with two-step clustering [17]. Alternatively, a Physarum-based hybrid optimization algorithm was suggested, which provides adaptable solutions for the loadshedding problem in a microgrid system [18].…”

Section: A Short-term Load Forecasting (Stlf)mentioning

confidence: 99%

“…where d e is one day to be extended; S d e = {set of g days, (10), the extended weight factors on day d e in cluster n are derived from the weight factors of the g most similar day candidates. Finally, the extended loads in cluster n are derived from the aggregated load and extended weight factors, as in (11).…”

Section: B Training Data Extension For Cldmentioning

confidence: 99%

“…Thus, training and validation data are insufficient for the individual buildings in cast that the test period is set to one year from Jan. 1st to Dec. 31st, 2017. However, the data can be extended based on the similar day approach, as given in (10) and (11). As the test set should not be known beforehand, the extended data were derived from known data for the period from May 15th to Dec. 31st, 2016.…”

Section: A Characteristic Load Analysis and Decompositionmentioning

confidence: 99%

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Hybrid Load Forecasting for Mixed-Use Complex Based on the Characteristic Load Decomposition by Pilot Signals

2019

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In this paper, a characteristic load decomposition (CLD)-based day-ahead load forecasting scheme is proposed for a mixed-use complex. The aggregated load of the complex is composed of the mixtures of different electricity usage patterns, and short-term load forecasting can be implemented by summing disaggregated sub-load predictions. However, tracing all usage patterns of sub-loads for prediction may be infeasible because of limited resources for measurement and analysis. To prevent this infeasibility, the proposed scheme focuses on effective decomposition using the sub-loads of typical characteristic load profiles and their representative pilot signals. Separate forecasts are obtained for the decomposed characteristic sub-loads using a hybrid scheme, which combines day-type conditioned linear prediction with long short-term memory regressions. Complex campus load data are considered to evaluate the proposed CLD-based hybrid forecasting. The evaluation results show that the proposed scheme outperforms conventional hybrid or similar-day-based forecasting approaches. Even when sub-load measurements are available only for a limited period, the CLD scheme can be applied for the extended training data through virtual disaggregations. INDEX TERMS Day-ahead load forecasting, time series analysis, long short-term memory, hybrid forecasting model, characteristic load decomposition, hierarchical load forecasting.

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Section: Introductionmentioning

confidence: 91%

Section: A Short-term Load Forecasting (Stlf)mentioning

confidence: 99%

Section: B Training Data Extension For Cldmentioning

confidence: 99%

Section: A Characteristic Load Analysis and Decompositionmentioning

confidence: 99%

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Hybrid Load Forecasting for Mixed-Use Complex Based on the Characteristic Load Decomposition by Pilot Signals

2019

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“…For instance, for about one third of the households, the models with a temperature input, i.e., M 1 and M 2 , clearly outperform the climatology M 0 at all levels. Identifying these households that benefit from the temperature input is quite straightforward: they are equipped with heating or cooling electrical devices, i.e., they have clear thermal sensitivity [34]. This sensitivity is measured by retrieving the correlation between the electricity demand and the outside temperature.…”

mentioning

confidence: 99%

Robust Day-Ahead Forecasting of Household Electricity Demand and Operational Challenges

et al. 2018

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In the recent years, several short-term forecasting models of household electricity demand have been proposed in the literature. This is partly due to emerging smart-grid applications, which require these kinds of forecasts to manage systems such as smart homes, prosumer aggregations, etc., and partly thanks to the availability of data from smart meters, which enable the development of such models. Since most models are academically developed, they often do not address challenges related to their implementation in a real-world environment. In the latter case, several issues arise, related to data quality and availability, which affect the operational performance and robustness of a forecasting system. In this paper, we design a hierarchical forecasting framework based on a total of 5 probabilistic models of varying complexity, after analyzing the respective performance and advantages of the models with an offline dataset. This multi-layered framework is necessary to address the various problematic situations occurring in practice and abide by the requirements for a real-world deployment. The forecasting system is deployed in a real-world case and evaluated here on data from 20 households. Field data, comprising forecasts and measurements, are analyzed for each household. A detailed comparison is drawn between the online and offline performances. Since a notable degradation is observed in the operational environment, we discuss at length the reasons for such an effect. We determine that the exact settings of the training and test periods are marginally responsible, but that the main cause is the intrinsic evolution of the demand time series, which hinders the forecasting performance. This evolution is due to unknown household characteristics that need to be monitored to provide more adaptable models.

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A Review of Methods for Long‐Term Electric Load Forecasting

Aditya,

Jaipuria,

Kumar Dadabada

2024

Journal of Forecasting

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Long‐term load forecasting (LTLF) has been a fundamental least‐cost planning tool for electric utilities. In the past, utilities were monopolies and paid less attention to uncertainty in their LTLF methodologies. Nowadays, such casualness is pricey in competitive markets because utilities need to examine the financial implications of forecast uncertainty for survival. Hence, the aim of this paper is to critique the LTLF research trends with a focus on uncertainty quantification (UQ). For this purpose, we examined 40 LTLF articles published between January 2003 and February 2021. We found that UQ is a nascent area of LTLF research. Our review found two approaches to UQ in LTLF: probabilistic scenario analysis and direct probabilistic methods. The former approach is more helpful to risk analysts but has major caveats in addressing interdependencies of socioeconomic and climate scenarios. We identified very little LTLF research that examines uncertainties associated with climate extremes, distributed generation resources, and demand‐side management. Lastly, there is enormous potential for mitigating financial risks by embracing asymmetric cost functions in LTLF research. Future LTLF researchers can work on these identified gaps to help utilities in risk estimation, cost‐reliability balancing, and estimation of reserve margin under climate change.

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A novel method for decomposing electricity feeder load into elementary profiles from customer information

Cited by 9 publications

References 21 publications

Hybrid Load Forecasting for Mixed-Use Complex Based on the Characteristic Load Decomposition by Pilot Signals

Hybrid Load Forecasting for Mixed-Use Complex Based on the Characteristic Load Decomposition by Pilot Signals

Robust Day-Ahead Forecasting of Household Electricity Demand and Operational Challenges

A Review of Methods for Long‐Term Electric Load Forecasting

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