Very-Short-Term Load Forecast for Individual Household Based on Behavior Pattern Induction

Abstract:

Electricity demand (load) forecasting has been recognizing as the key issue for achieving economic, reliable, and secure power system operation and planning. In the existing studies, two indentations are found in application level and methodology level, respectively. In the application level, the load forecasting for individual household is few. Most applications focus on large spatial region. In the methodology level, the importance of user daily schedule pattern is ignored in the development of load forecasting methods. In this study, a novel approach to model the load of individual household based on context information and daily schedule patterns is proposed. The daily schedule pattern types are induced from daily electricity consumption behaviors, and context features from several sources are collected and used to establish a rule set for classifying a given day into a specific behavior pattern type. Also, an electricity consumption volume prediction model is built for each behavior pattern type to predict the load of specific time point with in a day. A household very-short-term load forecasting problem which comes from Taiwan is implemented in this study. The results show that the proposed approach can get a better accuracy than other methods.

• Periodical: Advanced Materials Research (Volume 918)
• Pages: 177-182
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.918.177
• Online since: April 2014
• Authors: Yu Hsiang Hsiao*
• Keywords: Context Features, Data Mining (DM), Load Forecast, User Behavior Pattern

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