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access icon openaccess Electric load data characterising and forecasting based on trend index and auto-encoders

This is an open access article published by the IET under the Creative Commons Attribution-NonCommercial-NoDerivs License (http://creativecommons.org/licenses/by-nc-nd/3.0/)
Received 14/08/2018, Accepted 31/08/2018, Published 21/09/2018

Electricity consumption data are collected more frequently by high-quality meters in smart grids. Therefore, the load data volume and length increase dramatically. On the other hand, for advanced market-based applications, e.g. demand response, load service entities hope to identify or classify users better. In this study, a trend-based load characterising approach is proposed. Firstly, the concept of the candlestick chart is utilised as an innovative tool for load description. In addition, electricity trend indexes, e.g. stochastic oscillator and moving average convergence/divergence, are introduced as parameters for load characterising. Secondly, the stacked auto-encoders are utilised to forecast the future load based on the input historical trend indexes. Case studies in Guangdong province demonstrate that the proposed trend-based method is more applicable than existing approaches both in physical significance and accuracy.

Inspec keywords: power markets; power engineering computing; power consumption; smart power grids; neural nets; load forecasting; demand side management; power system economics

Other keywords: load data volume; stacked auto-encoders; electric load data characterization; trend-based method; load description; smart grids; advanced market-based applications; electricity consumption data; stochastic oscillator; high-quality meters; moving average convergence; trend-based load characterising approach; demand response; load service entities; input historical trend indexes

Subjects: Knowledge engineering techniques; Power system management, operation and economics; Power engineering computing; Neural computing techniques; Power system planning and layout

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