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E. Xypolytou, M. Meisel, T. Sauter:
"Short-term Electricity Consumption Forecast with Artificial Neural Networks - A Case Study of Office Buildings";
Vortrag: 12th IEEE PES PowerTech Conference, Manchester, UK; 18.06.2017 - 22.06.2017; in: "PowerTech Manchester 2017", IEEE (Hrg.); IEEE, (2017), S. 1 - 6.

To achieve climate and decarbonisation goals, electricity grid participants, such as buildings, must reduce their footprint trough renewable generation. Introducing storages can help buffering the fluctuating nature of renewable energy sources, but only with future knowledge of consumption and generation, a battery can be scaled sensibly to economically viable options. An efficient energy management system and accurate energy forecasts are necessary to proactively work within battery limits, to provide a short-term (day-ahead or hour-ahead) energy production plan which can then be utilised for demand response applications like load peak minimisation, self-consumption optimisation, intelligent energy storage and predictive control. Focus of this work is the accurate energy consumption prediction of office buildings and a case study, based on measurement data. The output of a prediction algorithm is intended to serve as input to predictive control models for a storage system, which enables the efficient energy storage and balanced consumption.

artificial neural networks, energy consumption forecast, measurement data, smart building

http://publik.tuwien.ac.at/files/publik_260286.pdf

Projektleitung Marcus Meisel:
Speicherintegration ins Büro(Office)gebäude in die Tech2Base