An R-based forecasting approach for efficient demand response strategies in autonomous micro-grids

Paraskevas Panagiotidis, Andrew Effraimis and George A Xydis

Energy & Environment, 2019, vol. 30, issue 1, 63-80

Abstract: The main aim of this work is to reduce electricity consumption for consumers with an emphasis on the residential sector in periods of increased demand. Efforts are focused on creating a methodology in order to statistically analyse energy demand data and come up with forecasting methodology/pattern that will allow end-users to organize their consumption. This research presents an evaluation of potential Demand Response programmes in Greek households, in a real-time pricing market model through the use of a forecasting methodology. Long-term Demand Side Management programs or Demand Response strategies allow end-users to control their consumption based on the bidirectional communication with the system operator, improving not only the efficiency of the system but more importantly, the residential sector-associated costs from the end-users’ side. The demand load data were analysed and categorised in order to form profiles and better understand the consumption patterns. Different methods were tested in order to come up with the optimal result. The Auto Regressive Integrated Moving Average modelling methodology was selected in order to ensure forecasts production on load demand with the maximum accuracy.

Keywords: Auto regressive integrated moving average; demand response; demand side management; forecasting; time-of-use (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:sae:engenv:v:30:y:2019:i:1:p:63-80

DOI: 10.1177/0958305X18787259

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