Data Driven Framework for Load Profile Generation in Medium Voltage Networks via Transfer Learning

Salazar, Mauricio Pérez; Dukovska, Irena; Nguyen, Phuong H.; Bernards, Raoul; Slootweg, J.G.

doi:10.1109/isgt-europe47291.2020.9248753

Cited by 2 publications

(3 citation statements)

References 14 publications

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“…Based on the historical consumption of each node, one can assign each bus to a cluster that belongs to the γ cluster via a supervised learning algorithm as in [16]. Alternatively, social demographic data of the area can be used to predict the cluster assignment for each node [37], which it can be more accurate using commercial datasets [38]. It is essential to notice that the cluster assignment to the node specifies which is the load profile model ( 10) is used for the PPF analysis.…”

Section: Scenario Generator and Simulationmentioning

confidence: 99%

Risk-Aware Operating Regions for PV-Rich Distribution Networks Considering Irradiance Variability

Duque

Giraldo

Vergara

et al. 2023

IEEE Trans. Sustain. Energy

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Section: Scenario Generator and Simulationmentioning

confidence: 99%

Risk-Aware Operating Regions for PV-Rich Distribution Networks Considering Irradiance Variability

Duque

Giraldo

Vergara

et al. 2023

IEEE Trans. Sustain. Energy

Self Cite

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“…In addition, DSOs are also installing smart meters at the end-customer level. The adoption of this Advanced Metering Infrastructure (AMI) improves monitor and control functionalities of distribution networks by DSOs aiming to mitigate the impact of the energy transition [2], [3]. Enriching data collected from AMI has driven the development of Machine Learning (ML) algorithms for load forecasting, which is widely studied in literature recently.…”

Section: Introductionmentioning

confidence: 99%

“…Other frequently encountered algorithms in literature are based on Support Vector Machines (SVM) and Decision Trees (DT). Driven by the increasing computational power and available data, more advanced ML algorithms, such as Deep Learning (DL), Reinforcement Learning (RL) and Transfer Learning (TL) are introduced to the field of load forecasting more recently to further improve the accuracy [2], [9]- [11]. The aim of a ML algorithm is to find the optimal correlation between a measured load and a set of input parameters, so called features, during the training process.…”

Section: Introductionmentioning

confidence: 99%

Improving Clustering-Based Forecasting of Aggregated Distribution Transformer Loadings With Gradient Boosting and Feature Selection

et al. 2022

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Load forecasting is more important than ever to enable new monitor and control functionalities of distribution networks aiming to mitigate the impact of the energy transition. Load forecasting at medium voltage (MV) level is becoming more challenging, because these load profiles become more stochastic due to the increasing penetration of photovoltaic (PV) generation in distribution networks. This work combines medium to low voltage (MV/LV) transformer loadings measured with advanced metering infrastructure (AMI) and machine learning (ML) algorithms to propose a new clustering based day-ahead aggregated load forecasting approach. This four-step approach improves the day-ahead load forecast of a city. First, MV/LV transformer loadings are clustered based on the shape of their load pattern. Second, a gradient boosting algorithm is used to forecast the load of each cluster and calculate the related feature importance. Third, feature selection is applied to improve the forecast accuracy of each cluster. Finally, the day-ahead load forecast of all clusters are aggregated. The case study presented uses 519 measured MV/LV transformer loadings in a city to perform 30 day-ahead load forecasts. Compared against the day-ahead aggregated load forecast without clustering, the average normalized root mean squared error (NRMSE) reduced 12.7 %, the average mean absolute percentage error (MAPE) reduced 18.2 % and the average Pearson Correlation Coefficient (PCC) increased 0.37 %. The 95 % confidence interval of the difference between the average NRMSE, MAPE and PCC without clustering and with the proposed method indicates a statistically significant improvement in accuracy.

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Data Driven Framework for Load Profile Generation in Medium Voltage Networks via Transfer Learning

Cited by 2 publications

References 14 publications

Risk-Aware Operating Regions for PV-Rich Distribution Networks Considering Irradiance Variability

Risk-Aware Operating Regions for PV-Rich Distribution Networks Considering Irradiance Variability

Improving Clustering-Based Forecasting of Aggregated Distribution Transformer Loadings With Gradient Boosting and Feature Selection

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