A Method to Analyzing and Clustering Aggregate Customer Load Profiles Based on PCA

Bosisio, Alessandro; Berizzi, Alberto; Vicario, Andrea; Morotti, Andrea; Greco, Bartolomeo; Iannarelli, Gaetano; Le, Dinh-Duong

doi:10.1109/gtsd50082.2020.9303098

Cited by 9 publications

(4 citation statements)

References 22 publications

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“…Clustering-based techniques streamline load forecasting by grouping similar load patterns, which aids in managing the variability from different energy sources and consumer behaviors. Techniques like K-means, hierarchical clustering, combined locally linear embedding (LLE), principal component analysis (PCA), and multi-layer perceptrons (MLPs), enhance accuracy, integrate renewable energy sources (RESs), aid demandside management (DSM), and bolster SG functions [15,[43][44][45][46]80]. Time series load forecasting (TSLF) methods utilize historical data, enhanced by advanced algorithms such as ARIMA and neural networks, to predict future demand.…”

Section: Comprehensive Approaches To Forecastingmentioning

confidence: 99%

Digitalization Processes in Distribution Grids: A Comprehensive Review of Strategies and Challenges

Aghahadi,

Bosisio,

Merlo

et al. 2024

Applied Sciences

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This systematic review meticulously explores the transformative impact of digital technologies on the grid planning, grid operations, and energy market dynamics of power distribution grids. Utilizing a robust methodological framework, over 54,000 scholarly articles were analyzed to investigate the integration and effects of artificial intelligence, machine learning, optimization, the Internet of Things, and advanced metering infrastructure within these key subsections. The literature was categorized to show how these technologies contribute specifically to grid planning, operation, and market mechanisms. It was found that digitalization significantly enhances grid planning through improved forecasting accuracy and robust infrastructure design. In operations, these technologies enable real-time management and advanced fault detection, thereby enhancing reliability and operational efficiency. Moreover, in the market domain, they support more efficient energy trading and help in achieving regulatory compliance, thus fostering transparent and competitive markets. However, challenges such as data complexity and system integration are identified as critical hurdles that must be overcome to fully harness the potential of smart grid technologies. This review not only highlights the comprehensive benefits but also maps out the interdependencies among the planning, operation, and market strategies, underlining the critical role of digital technologies in advancing sustainable and resilient energy systems.

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Section: Comprehensive Approaches To Forecastingmentioning

confidence: 99%

Digitalization Processes in Distribution Grids: A Comprehensive Review of Strategies and Challenges

Aghahadi,

Bosisio,

Merlo

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

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“…In [29], the daily consumption of secondary EV substations is examined using a PCA model. The original data is transformed into three new orthogonal variables representing 96% of the total variance of the substations data.…”

Section: Principal Component Analysis (Pca)mentioning

confidence: 99%

“…These PCA components are used to classify substations' data sets to standard demand profiles. While the research presented in [29] employs PCA for labeling EVs' demand profile, article [30] adopts the output of PCA model to establish a polynomial of PCA components to estimate future values of EVs' energy consumption. For this purpose, the model coefficients are set such that these weights demonstrate the importance of the corresponding variable relative to the output of the model.…”

Section: Principal Component Analysis (Pca)mentioning

confidence: 99%

Electric Vehicle Energy Demand Prediction Techniques: An In-Depth and Critical Systematic Review

Marzbani,

Osman,

Hassan

2023

IEEE Access

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Accurate forecast of electric vehicle energy demand is vital for maintaining the stability and reliability of power systems. With the increasing prevalence of electric vehicles in transportation systems, the anticipating demand surges with precision in terms of timing and location is becoming ever more critical for utilities to guarantee sufficient supply. The intermittent and stochastic nature of electric vehicle electricity consumption is a significant challenge in accurately forecasting of electric vehicles demand. As a result, there is a growing field of research focused on developing models that can effectively capture and interpret such complex data. In improving the potential of accurate prediction models, conducting a comprehensive review of literature, examining current research overviews, and exploring potential expansions and extensions of models are all critical components. In this review, a comprehensive overview of prior research conducted for forecasting electric vehicle energy demand is presented, including a detailed examination of the benefits and drawbacks of the methods used. Additionally, potential gaps in the field are identified, and recommendations for future research directions are provided.

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“…The first step is to exclude the influence of random components and extract load curve features from historical data. This section adopts principal component analysis (PCA) [33][34][35], which can separate the commonness and difference from data vectors and retain the main information of the data.…”

Section: Disturbance Data Processingmentioning

confidence: 99%

Medium- and Long-Term Trading Strategies for Large Electricity Retailers in China’s Electricity Market

et al. 2022

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In the rapid promotion of China’s electricity spot market, a large number of electricity retailers and large consumers participate in power trading, of which medium- and long-term power trading accounts for a large proportion. In the electricity spot market, the previous medium- and long-term transactions need to be closely combined with the current spot market transaction settlement rules. This paper analyzes the trading strategy of large retailers in the power market. In order to effectively reduce the total electricity cost, it is necessary to optimize the medium- and long-term transactions based on three aspects: electricity quantity and benchmark price decisions of medium- and long-term contracts, the daily electricity decomposition method in the day-ahead (DA) market, and the daily load curve decomposition strategy. According to load history characteristics that are extracted by the X12 method, daily electricity is decomposed from the medium- and long-term electricity quantity in the DA market. This paper introduces three methods of decomposing the daily load curve and proves that the particle swarm algorithm is the best method for effectively minimizing the cost in the DA market. Through analyzing the total electricity cost change pattern, we prove that the basic component of decision making is the relative relationship between the electricity price of medium- and long-term contracts and the equivalent kWh price of medium- and long-term electricity in the DA market, which is determined by the decomposition daily curve method. If the equivalent kilowatt-hour price obtained by the decomposition method in the DA market is greater than the electricity price of medium- and long-term contracts, the larger the electrical energy of medium- and long-term contracts, the lower the costs. Based on the above principles, electricity retailers can carry out planning for medium- and long-term transactions, as well as the decomposition and declaration of the daily electricity quantities and daily load curves.

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A Method to Analyzing and Clustering Aggregate Customer Load Profiles Based on PCA

Cited by 9 publications

References 22 publications

Digitalization Processes in Distribution Grids: A Comprehensive Review of Strategies and Challenges

Digitalization Processes in Distribution Grids: A Comprehensive Review of Strategies and Challenges

Electric Vehicle Energy Demand Prediction Techniques: An In-Depth and Critical Systematic Review

Medium- and Long-Term Trading Strategies for Large Electricity Retailers in China’s Electricity Market

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