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Individual and Aggregate Electrical Load Forecasting: One for All and All for One

Published: 14 July 2015 Publication History

Abstract

Electrical load forecasting is an important task for utility companies, in order to plan future production and to increase the efficiency of the distribution network. Although load forecasting at the aggregate level has been extensively studied in existing literature, forecasts for individual consumers have been shown to be prone to errors. This paper deals with the problem of electrical load forecasting at multiple scales, from individual consumers to the network as a whole. We use smart meter data from carefully selected sets of consumers for this purpose.
First, we consider the problem of forecasting the load for individual consumers at the outermost nodes of the distribution network. We propose an algorithm which considers external available information like calendar or weather contexts along with the energy consumption profiles of different consumers for accurate mid-term and short-term load forecasting. Multiple aggregation approaches are considered for utility level forecasting, in order to characterize their error properties. We show that careful clustering of consumers for aggregation can result in smaller errors. We experiment with two public data sets for demonstrating the advantages of the proposed method over the state-of-the-art approaches.

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      cover image ACM Conferences
      e-Energy '15: Proceedings of the 2015 ACM Sixth International Conference on Future Energy Systems
      July 2015
      334 pages
      ISBN:9781450336093
      DOI:10.1145/2768510
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      Published: 14 July 2015

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      Author Tags

      1. clustering
      2. context information
      3. individual time series
      4. load forecasting
      5. short and medium term

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      • (2023)A Novel Clustering-based Forecast Framework: The Clusters with Competing Configurations ApproachAcademic Platform Journal of Engineering and Smart Systems10.21541/apjess.126661011:3(151-162)Online publication date: 30-Sep-2023
      • (2023)Precision and Accuracy Co-Optimization-Based Demand Response Baseline Load Estimation Using Bidirectional DataIEEE Transactions on Smart Grid10.1109/TSG.2022.319238614:1(266-276)Online publication date: Jan-2023
      • (2022)A compositional kernel based gaussian process approach to day-ahead residential load forecastingEnergy and Buildings10.1016/j.enbuild.2021.111459254(111459)Online publication date: Jan-2022
      • (2021)An Ensemble Method for Aggregated Baseline Load Estimation: From Probabilistic Perspective2021 IEEE 5th Conference on Energy Internet and Energy System Integration (EI2)10.1109/EI252483.2021.9712884(3138-3142)Online publication date: 22-Oct-2021
      • (2020)Unearthing Details of Time Series of LoadProceedings of the Eleventh ACM International Conference on Future Energy Systems10.1145/3396851.3402367(419-427)Online publication date: 12-Jun-2020
      • (2020)Demand Smoothing in Military Microgrids Through Coordinated Direct Load ControlIEEE Transactions on Smart Grid10.1109/TSG.2019.294527811:3(1917-1927)Online publication date: May-2020
      • (2020)Improving Aggregated Load Forecasting Using Evidence Accumulation k-Shape Clustering2020 IEEE Power & Energy Society General Meeting (PESGM)10.1109/PESGM41954.2020.9281744(1-5)Online publication date: 2-Aug-2020
      • (2020)Hierarchical Model-Free Transactional Control of Building Loads to Support Grid ServicesIEEE Access10.1109/ACCESS.2020.30411808(219367-219377)Online publication date: 2020
      • (2020)Electrical load forecasting in disaggregated levels using Fuzzy ARTMAP artificial neural network and noise removal by singular spectrum analysisSN Applied Sciences10.1007/s42452-020-2988-52:7Online publication date: 15-Jun-2020
      • (2019)Understanding the effects of temporal energy-data aggregation on clustering qualityit - Information Technology10.1515/itit-2019-001461:2-3(111-123)Online publication date: 24-Oct-2019
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