Scenario generation of residential electricity consumption through sampling of historical data

The low-voltage grid (LVG) needs to be reinforced to handle the increased load due to the transition towards renewable energy. Doing this optimally requires knowledge of typical currents and voltages throughout the grid, which are unknown. They can be calculated from the grid layout and electricity consumption time series of each consumer, but for many consumers this time series is unknown. To alleviate this problem, we have developed two techniques to generate accurate and realistic daylong electricity consumption time series (scenarios) for a given consumer. Both techniques generate scenarios by sampling from historical consumption measurements of a limited set of consumers, considering available information about consumers (e.g., total yearly consumption) and days (e.g., weather). The first technique uses expert knowledge to define this sampling procedure, whereas the second learns it automatically using machine learning. The quality of the generated scenarios is evaluated by estimating how well the distributions of predicted and observed time series match, conditional on the available information. The data-driven technique performs better than the expert-based technique and, contrary to the latter, can easily be applied to datasets with different attributes without requiring any domain knowledge. Both proposed techniques outperform random sampling (the prevalent approach in existing LVG studies) and standard load profiles (commonly used by distribution system operators (DSOs)). Most of the improvement is obtained by including consumer attributes, whereas daily attributes lead to little or no performance improvement. The proposed techniques can help DSOs estimate the state of the LVG more accurately, thereby reducing investment costs.