Definition of Residential Power Load Profiles Clusters Using Machine Learning and Spatial Analysis

Flor, Mario; Herraiz, S.; Contreras, Iván

doi:10.3390/en14206565

Cited by 3 publications

(2 citation statements)

References 33 publications

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“…Studies on energy consumption profiles using smart meters are available for countries such as Qatar [13], where a method is proposed to estimate demand based on high-resolution load profile data. In Ecuador [14], the authors proposed classification models to identify temporal grouping patterns and neural network models to predict energy demand. Evidence for Korea [5] shows that in addition to classification algorithms, socio-demographic information is also included in consumption predictions at the neighborhood level.…”

Section: Empirical Literature On Load Profiles or Consumption Patternsmentioning

confidence: 99%

Analysis and Modeling of Residential Energy Consumption Profiles Using Device-Level Data: A Case Study of Homes Located in Santiago de Chile

Verdejo,

Fucks Jara,

Castillo

et al. 2023

Sustainability

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The advancement of technology has significantly improved energy measurement systems. Recent investment in smart meters has enabled companies and researchers to access data with the highest possible temporal disaggregation, on a minute-by-minute basis. This research aimed to obtain data with the highest possible temporal and spatial disaggregation. This was achieved through a process of energy consumption measurements for six devices within seven houses, located in different communes (counties) of the Metropolitan Region of Chile. From this process, a data panel of energy consumption of six devices was constructed for each household, observed in two temporal windows: one quarterly (750,000+ observations) and another semi-annual (1,500,000+ observations). By applying a panel data econometric model with fixed effects, calendar-temporal patterns that help explain energy consumption in each of these two windows have been studied, obtaining explanations of over 80% in some cases, and very low in others. Sensitivity analyses show that the results are robust in a short-term temporal horizon and provide a practical methodology for analyzing energy consumption determinants and load profiles with panel data. Moreover, to the authors’ knowledge, these are the first results obtained with data from Chile. Therefore, the findings provide key information for the planning of production, design of energy market mechanisms, tariff regulation, and other relevant energy policies, both at local and global levels.

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Section: Empirical Literature On Load Profiles or Consumption Patternsmentioning

confidence: 99%

Analysis and Modeling of Residential Energy Consumption Profiles Using Device-Level Data: A Case Study of Homes Located in Santiago de Chile

Verdejo,

Fucks Jara,

Castillo

et al. 2023

Sustainability

View full text Add to dashboard Cite

show abstract

“…Soft clustering is more suitable to be applied because it represents the state of the data and the relationship between the variables. However, the analysis of the regional (spatial) electrical load profile that has been carried out by [23] only in which the clustering was done by using neural network.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy C-means clustering based on micro-spatial analysis for electricity load profile characterization

Senen

Putri

Jamian

et al. 2023

IJEECS

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As the rising of electricity demand, electricity load profile characterization (ELPC) is the integral aspect in planning, operating system, and distribution network development. The approach in the existing ELPC is still relatively macro in nature and does not involve other aspects outside the electricity variable, so the results tend to be biased for areas experiencing rapid land use changes. Therefore, this paper proposes an ELPC approach based on micro-spatial. Microspatial analysis is done by dividing area in the form of the smallest grids involving various electrical, demographic, geographic and socio-economic variables, which are then grouped using adaptive clustering based on fuzzy C-means (FCM). The adaptive clustering algorithm is proven to be able to determine the degree of membership of each grid data against each cluster with the ability to determine the number of clusters automatically according to the attribute data provided. The ELPC results which consist of 5 clusters are then analyzed using descriptive statistic, plotted, and mapped to obtain more accurate and realistic load characteristics in accordance with the pattern and geographical conditions of the region, so that the results can be used as a reference in load forecasting, network development, and distributed generation (DG) integration.

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Enhancing peak prediction in residential load forecasting with soft dynamic time wrapping loss functions

Chen,

Obrecht,

Kuznik

2024

ICA

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Short-term residential load forecasting plays a crucial role in smart grids, ensuring an optimal match between energy demands and generation. With the inherent volatility of residential load patterns, deep learning has gained attention due to its ability to capture complex nonlinear relationships within hidden layers. However, most existing studies have relied on default loss functions such as mean squared error (MSE) or mean absolute error (MAE) for neural networks. These loss functions, while effective in overall prediction accuracy, lack specialized focus on accurately predicting load peaks. This article presents a comparative analysis of soft-DTW loss function, a smoothed formulation of Dynamic Time Wrapping (DTW), compared to other commonly used loss functions, in order to assess its effectiveness in improving peak prediction accuracy. To evaluate peak performance, we introduce a novel evaluation methodology using confusion matrix and propose new errors for peak position and peak load, tailored specifically for assessing peak performance in short-term load forecasting. Our results demonstrate the superiority of soft-DTW in capturing and predicting load peaks, surpassing other commonly used loss functions. Furthermore, the combination of soft-DTW with other loss functions, such as soft-DTW + MSE, soft-DTW + MAE, and soft-DTW + TDI (Time Distortion Index), also enhances peak prediction. However, the differences between these combined soft-DTW loss functions are not substantial. These findings highlight the significance of utilizing specialized loss functions, like soft-DTW, to improve peak prediction accuracy in short-term load forecasting.

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Definition of Residential Power Load Profiles Clusters Using Machine Learning and Spatial Analysis

Cited by 3 publications

References 33 publications

Analysis and Modeling of Residential Energy Consumption Profiles Using Device-Level Data: A Case Study of Homes Located in Santiago de Chile

Analysis and Modeling of Residential Energy Consumption Profiles Using Device-Level Data: A Case Study of Homes Located in Santiago de Chile

Fuzzy C-means clustering based on micro-spatial analysis for electricity load profile characterization

Enhancing peak prediction in residential load forecasting with soft dynamic time wrapping loss functions

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