Abstract
The similarity analysis of the monthly electric energy demand time series sequence patterns are shown. The similarity-based forecasting models are allowed to be created because a strong relationship between input and output patterns exists. The chi-square test and the correlation tables were calculated for a few definitions of patterns.
Highlights
The monthly load time series for the national power systems are characterised by annual periodic variations
In the case studied in this paper, patterns are the points in a sequence of the monthly loads of the Polish power system (1998-2014) processed using specific functions
The pattern similarity-based forecasting methodology relies on the assumption that if the input patterns xa and xb are similar, similar are the output patterns ya and yb, which represent the time-series fragments following the fragments represented by the xa and xb patterns
Summary
The monthly load time series for the national power systems are characterised by annual periodic variations. The analogies between time-series sequences with periodic variations are used successfully by the pattern similarity-based forecasting models. The second category consists of the autonomous modelling approach This type requires a smaller set of inputs: firstly past loads and optionally e.g. weather variables. Models from this category are more suited for stable economies [5]. This group is represented by classical forecasting e.g. ARIMA or linear regression [7], and computational intelligence methods, like neural networks [8]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
