Explaining the distribution of energy consumption at slow charging infrastructure for electric vehicles from socio-economic data

• URL: http://arxiv.org/abs/2006.01672v2
• Date: Wed, 3 Jun 2020 06:56:58 GMT
• Title: Explaining the distribution of energy consumption at slow charging infrastructure for electric vehicles from socio-economic data
• Authors: Milan Straka, Rui Carvalho, Gijs van der Poel, \v{L}ubo\v{s} Buzna
• Abstract summary: We develop a data-centric approach enabling to analyse which activities, function, and characteristics of the environment surrounding the slow charging infrastructure impact the distribution of electricity consumed at slow charging infrastructure.
• Score: 2.1294627833637576
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Here, we develop a data-centric approach enabling to analyse which activities, function, and characteristics of the environment surrounding the slow charging infrastructure impact the distribution of the electricity consumed at slow charging infrastructure. To gain a basic insight, we analysed the probabilistic distribution of energy consumption and its relation to indicators characterizing charging events. We collected geospatial datasets and utilizing statistical methods for data pre-processing, we prepared features modelling the spatial context in which the charging infrastructure operates. To enhance the statistical reliability of results, we applied the bootstrap method together with the Lasso method that combines regression with variable selection ability. We evaluate the statistical distributions of the selected regression coefficients. We identified the most influential features correlated with energy consumption, indicating that the spatial context of the charging infrastructure affects its utilization pattern. Many of these features are related to the economic prosperity of residents. Application of the methodology to a specific class of charging infrastructure enables the differentiation of selected features, e.g. by the used rollout strategy. Overall, the paper demonstrates the application of statistical methodologies to energy data and provides insights on factors potentially shaping the energy consumption that could be utilized when developing models to inform charging infrastructure deployment and planning of power grids.

Related papers

• Attention-based Citywide Electric Vehicle Charging Demand Prediction Approach Considering Urban Region and Dynamic Influences [5.687001127686438]
  We propose an attention-based heterogeneous data fusion approach (ADF) for electric vehicle charging demand prediction. To learn non-pairwise relationships, we cluster service areas by the types and numbers of points of interest in the areas. We demonstrate the impact of dynamic influences on prediction results in different areas of the city and the effectiveness of our clustering method.
  arXiv  Detail & Related papers  (2024-10-24T14:19:38Z)
• Leveraging Graph Neural Networks to Forecast Electricity Consumption [3.157383076370605]
  This research work offers a novel approach that extends beyond the conventional Generalized Additive Model framework. We introduce a range of methods for inferring graphs tailored to consumption forecasting, along with a framework for evaluating the developed models. We conduct experiments on electricity forecasting, in both a synthetic and a real framework considering the French mainland regions.
  arXiv  Detail & Related papers  (2024-08-30T15:54:50Z)
• Graph Learning under Distribution Shifts: A Comprehensive Survey on Domain Adaptation, Out-of-distribution, and Continual Learning [53.81365215811222]
  We provide a review and summary of the latest approaches, strategies, and insights that address distribution shifts within the context of graph learning. We categorize existing graph learning methods into several essential scenarios, including graph domain adaptation learning, graph out-of-distribution learning, and graph continual learning. We discuss the potential applications and future directions for graph learning under distribution shifts with a systematic analysis of the current state in this field.
  arXiv  Detail & Related papers  (2024-02-26T07:52:40Z)
• Scaling Laws for Sparsely-Connected Foundation Models [70.41266138010657]
  We explore the impact of parameter sparsity on the scaling behavior of Transformers trained on massive datasets. We identify the first scaling law describing the relationship between weight sparsity, number of non-zero parameters, and amount of training data.
  arXiv  Detail & Related papers  (2023-09-15T16:29:27Z)
• Benchmarks and Custom Package for Energy Forecasting [55.460452605056894]
  Energy forecasting aims to minimize the cost of subsequent tasks such as power grid dispatch. In this paper, we collected large-scale load datasets and released a new renewable energy dataset. We conducted extensive experiments with 21 forecasting methods in these energy datasets at different levels under 11 evaluation metrics.
  arXiv  Detail & Related papers  (2023-07-14T06:50:02Z)
• An Energy Consumption Model for Electrical Vehicle Networks via Extended Federated-learning [50.85048976506701]
  This paper proposes a novel solution to range anxiety based on a federated-learning model. It is capable of estimating battery consumption and providing energy-efficient route planning for vehicle networks.
  arXiv  Detail & Related papers  (2021-11-13T15:03:44Z)
• Characterizing Residential Load Patterns by Household Demographic and Socioeconomic Factors [3.3880352469193475]
  This paper aims to characterize and estimate users' load patterns based on their demographic and socioeconomic information. We develop a deep neural network (DNN) to analyze the relationship between users' load patterns and their demographic and socioeconomic features.
  arXiv  Detail & Related papers  (2021-06-04T15:01:35Z)
• Decision-making Oriented Clustering: Application to Pricing and Power Consumption Scheduling [61.062312682535755]
  We formulate the framework of decision-making oriented clustering and propose an algorithm providing a decision-based partition of the data space and good representative decisions. By applying this novel framework and algorithm to a typical problem of real-time pricing and that of power consumption scheduling, we obtain several insightful analytical results.
  arXiv  Detail & Related papers  (2021-06-02T08:41:04Z)
• Uncover Residential Energy Consumption Patterns Using Socioeconomic and Smart Meter Data [2.9340691207364786]
  We analyze the real-world smart meter data and extract load patterns using K-Medoids clustering. We develop an analytical framework with feature selection and deep learning models to estimate the relationship between load patterns and socioeconomic features.
  arXiv  Detail & Related papers  (2021-04-12T01:57:14Z)
• Investigating Underlying Drivers of Variability in Residential Energy Usage Patterns with Daily Load Shape Clustering of Smart Meter Data [53.51471969978107]
  Large-scale deployment of smart meters has motivated increasing studies to explore disaggregated daily load patterns. This paper aims to shed light on the mechanisms by which electricity consumption patterns exhibit variability.
  arXiv  Detail & Related papers  (2021-02-16T16:56:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.