Machine Learning Approach for Flexibility Characterisation of Residential Space Heating

Due to an increasing share of renewable energy sources the balancing of energy production and consumption is getting a lot of interest considering future smart grids. In this context, many investigations on demand-response programs are being conducted to achieve flexibility from different energy storages and loads. As space heating is an important schedulable load for flexibility simulations, there are different modelling approaches due to its interdisciplinary nature. Models can be built from the civil engineering or electrical engineering point of view, depending on the computational expense and accuracy level. Scheduling optimizations need a lot of simulations, preferably with computationally light models. Thus, this work will use a computationally light neural network load prediction model for space heating which is based on a detailed civil engineering model. Simulations with different scheduling times were conducted to see the long- and short-term effects of the demand response action. Results show, that applying the same demand response action at different times results in different behaviors of the system resp. energy consumption, which requires further studies for developing optimized scheduling methods.