Stylianos Sp. Pappas

TL;DR: In this paper, an AutoRegressive Moving Average (ARMA) model is fitted on the data off-line using the Akaike Corrected Information Criterion (AICC) to fit the data in a successful manner.

TL;DR: In this paper, a new method for electricity demand load forecasting using the multi-model partitioning theory is presented, and compared with three other well established time series analysis techniques namely Corrected Akaike Information Criterion (AICC), AKAike's information Criterion(AIC) and Schwarz's Bayesian Information Criteria (BIC).

TL;DR: In this paper, an adaptive method based on the multi-model partitioning algorithm (MMPA) was proposed for short-term electricity load forecasting using real data, where the grid utilization was initially modeled using a multiplicative seasonal ARIMA (autoregressive integrated moving average) model.

TL;DR: In this article , detailed scalability and replicability plans have been developed to facilitate the adoption of innovation technologies in the pan-EU market, and the FLEXITRANSTORE project integrates hardware and software solutions in all areas of the transmission system and wholesale markets, unleashing the potential for full flexibility of power systems and promoting the penetration of renewable energy sources and pan EU markets.

TL;DR: A new method based on the multi-model partitioning theory which is also applicable to on line/adaptive operation, is used for the solution of the problem of modeling the variation of the grounding resistance during the year.