Multi-Step Energy Demand and Generation Forecasting with Confidence Used for Specification-Free Aggregate Demand Optimization

Nikolaos Kolokas, Dimosthenis Ioannidis and Dimitrios Tzovaras
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Nikolaos Kolokas: Centre for Research and Technology Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece
Dimosthenis Ioannidis: Centre for Research and Technology Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece
Dimitrios Tzovaras: Centre for Research and Technology Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece

Energies, 2021, vol. 14, issue 11, 1-36

Abstract: Energy demand and generation are common variables that need to be forecast in recent years, due to the necessity for energy self-consumption via storage and Demand Side Management. This work studies multi-step time series forecasting models for energy with confidence intervals for each time point, accompanied by a demand optimization algorithm, for energy management in partly or completely isolated islands. Particularly, the forecasting is performed via numerous traditional and contemporary machine learning regression models, which receive as input past energy data and weather forecasts. During pre-processing, the historical data are grouped into sets of months and days of week based on clustering models, and a separate regression model is automatically selected for each of them, as well as for each forecasting horizon. Furthermore, the multi-criteria optimization algorithm is implemented for demand scheduling with load shifting, assuming that, at each time point, demand is within its confidence interval resulting from the forecasting algorithm. Both clustering and multiple model training proved to be beneficial to forecasting compared to traditional training. The Normalized Root Mean Square Error of the forecasting models ranged approximately from 0.17 to 0.71, depending on the forecasting difficulty. It also appeared that the optimization algorithm can simultaneously increase renewable penetration and achieve load peak shaving, while also saving consumption cost in one of the tested islands. The global improvement estimation of the optimization algorithm ranged approximately from 5% to 38%, depending on the flexibility of the demand patterns.

Keywords: time series; forecasting; demand; generation; prediction interval; optimization; demand response; Demand Side Management; flexibility; smart islands (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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