Combined PV Power and Load Prediction for Building-Level Energy Management Applications
| Autor: | Benedikt Hanke, Matthias Grottke, Karsten von Maydell, Jan-Simon Telle, Thomas Steens, Nailya Maitanova |
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| Rok vydání: | 2020 |
| Předmět: |
Schedule
Artificial neural network Mean squared error Computer science business.industry Energy management 020209 energy Photovoltaic system 020206 networking & telecommunications 02 engineering and technology Residual load Management in commercial buildings Renewable energy Reliability engineering Combined PV-power and load prediction 0202 electrical engineering electronic engineering information engineering Electricity business machin learning approaches optimized charging of battery electric vehicles |
| Zdroj: | EVER |
| DOI: | 10.1109/ever48776.2020.9243026 |
| Popis: | In order to successfully integrate renewable energy technologies, the requirements of local energy management systems are becoming increasingly complex, as is the sector integration of electricity, heat and transportation. To address this, this study investigated the combination of machine learning-based PV power and load demand prediction approaches to forecast residual load at the building level. The forecast accuracy, seasonal dependencies and the effects of single forecasts on the residual load were evaluated by means of three different metrics, namely: mean absolute error (MAE), root-mean-square error (RMSE) and the mean absolute scaled error (MASE). The applicability of the combined forecast was tested via a case study of integrated battery-electric vehicles and a PV system in an existing commercial building. The results show how the residual load forecast can help schedule grid-friendly charging demand and optimize PV self-consumption. |
| Databáze: | OpenAIRE |
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