Behavioral Analysis and Pattern Validation

Abstract

After reading this chapter you should be able to:

• Understand the different ML techniques applied to energy demand management

• Understand the main tools available for demand aggregation and forecasting

• Apply different behavioral analyses to energy pattern classification

• Understand demand forecasting at the individual and aggregated demand levels.

Encouraging consumers in responsible consumption is essential to sustainable energy use. Effective demand-side planning is based on categorizing consumers and their demand behaviors and in-depth analysis of reallocation responses. Consequently, major streams in residential energy consumption research are ecological trends and consumer behaviors. This chapter analyzes consumption linkages, estimates energy footprints according to consumer preferences, describes the analysis and definition of the behavioral patterns of consumers participating in aggregated DR services, and demonstrates how consumer profiles can influence validation of these services. Categorizing consumers according to their demand preferences and understanding consumption in different scenarios improves planning and decision-making by both the consumer and the energy manager. Also described is optimal demand forecasting based on clustering models.