Diagnostic and Input Selection Tool applied on Weather Variables for Studies of Short-Term Load Forecasting

Silva, L. N.; Abaide, Alzenira da Rosa; Negri, Valeria; Capeletti, Marcelo Bruno; Lopes, Luís Felipe Dias; Cardoso, Gilmar dos Santos

doi:10.1109/mps.2019.8759658

Cited by 3 publications

(2 citation statements)

References 17 publications

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“…In fact, the temperature influence varies among the regions due to a different use of cooling and heating devices. This effect is also discussed by De Felice et al (De Felice et al, 2013) and Silva et al in (Silva et al, 2019b) for the climate in Italy and South America respectively. Furthermore, in their study De Felice et al discovered that from all included weather variables only the temperature demonstrated an evident influence on the daily load variation.…”

Section: Value Of Exogenous Data For Different Load Forecast Parameterssupporting

confidence: 58%

“…As a result, numerous approaches tried to increase the forecast accuracy by extending the input variables with information from various weather variables (Rahman and Hazim, 1993;Mirasgedis et al, 2006;Howe, 2010;Chu et al, 2011). Recently, Silva et al even defined weather variables and mainly temperature, humidity and wind speed as the most significant exogenous influences in STLF (Silva et al, 2019b).…”

Section: Exogenous Data In Load Forecasting: Characterizationmentioning

confidence: 99%

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Exogenous Data for Load Forecasting: A Review

Christen

Mazzola

Denzler

et al. 2020

Proceedings of the 12th International Joint Conference on Computational Intelligence

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Electrical power load forecasting defines strategies for utilities, power producers and individuals that participate in a smart grid. While it is well established in planning processes for production and utilities, the importance of accurate forecasting increases for individuals. The ongoing deregulation of the electricity market enables energy trading by individuals, requiring an accurate estimation of the production and consumption. Research on forecast for aggregated demand shows that including features for the forecast from sources, called exogenous, additional to the purely historical consumption data allows to obtain higher accuracy. In fact, their usage demonstrated to be able to explain the large variability observed in the power demand, taking into account the individual influences. Anyway, the influence of exogenous data is hardly investigated for individual forecasting, due to the minor prevalence of this analysis to date. This review shows the benefit of exogenous data usage and the necessity of detailed research on the input features and their influence on detailed, individual level, forecasts of power demand. Eventually, this contribution is concluded by the presentation of open issues and research directions for electric smart communities that the authors would like to address.

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Section: Value Of Exogenous Data For Different Load Forecast Parameterssupporting

confidence: 58%

Section: Exogenous Data In Load Forecasting: Characterizationmentioning

confidence: 99%

Exogenous Data for Load Forecasting: A Review

Christen

Mazzola

Denzler

et al. 2020

Proceedings of the 12th International Joint Conference on Computational Intelligence

View full text Add to dashboard Cite

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Descriptive Data Analysis of Weather Inputs for Non-Technical Losses Detection System

Capeletti

Abaide

Hammerschmitt

et al. 2021

2021 9th International Conference on Modern Power Systems (MPS)

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Identification of Nontechnical Losses in Distribution Systems Adding Exogenous Data and Artificial Intelligence

et al. 2022

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Nontechnical losses (NTL) are irregularities in the consumption of electricity and mainly caused by theft and fraud. NTLs can be characterized as outliers in historical data series. The use of computational tools to identify outliers is the subject of research around the world, and in this context, artificial neural networks (ANN) are applicable. ANNs are machine learning models that learn through experience, and their performance is associated with the quality of the training data together with the optimization of the model’s architecture and hyperparameters. This article proposes a complete solution (end-to-end) using the ANN multilayer perceptron (MLP) model with supervised classification learning. For this, data mining concepts are applied to exogenous data, specifically the ambient temperature, and endogenous data from energy companies. The association of these data results in the improvement of the model’s input data that impact the identification of consumer units with NTLs. The test results show the importance of combining exogenous and endogenous data, which obtained a 0.0213 improvement in ROC-AUC and a 6.26% recall (1).

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Diagnostic and Input Selection Tool applied on Weather Variables for Studies of Short-Term Load Forecasting

Cited by 3 publications

References 17 publications

Exogenous Data for Load Forecasting: A Review

Exogenous Data for Load Forecasting: A Review

Descriptive Data Analysis of Weather Inputs for Non-Technical Losses Detection System

Identification of Nontechnical Losses in Distribution Systems Adding Exogenous Data and Artificial Intelligence

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