text: The increasing use of renewable energy sources with variable output, such as solar photovoltaic and wind power generation, calls for Smart Grids that effectively manage flexible loads and energy storage. The ability to forecast consumption at different locations in distribution systems will be a key capability of Smart Grids. The goal of this paper is to benchmark state-of-the-art methods for forecasting electricity demand on the household level across different granularities and time scales in an explorative way, thereby revealing potential shortcomings and find promising directions for future research in this area. We apply a number of forecasting methods including ARIMA, neural networks, and exponential smoothening using several strategies for training data selection, in particular day type and sliding window based strategies. We consider forecasting horizons ranging between 15 minutes and 24 hours. Our evaluation is based on two data sets containing the power usage of individual appliances at second time granularity collected over the course of several months. The results indicate that forecasting accuracy varies significantly depending on the choice of forecasting methods strategy and the parameter configuration. Measured by the Mean Absolute Percentage Error (MAPE), the considered state-of-the-art forecasting methods rarely beat corresponding persistence forecasts. Overall, we observed MAPEs in the range between 5 and >100 . The average MAPE for the first data set was 30 , while it was 85 for the other data set. These results show big room for improvement. Based on the identified trends and experiences from our experiments, we contribute a detailed discussion of promising future research.
  @cite_18: Demand response (DR) is becoming an integral part of power system and market operations. Smart grid technologies will further increase the use of DR in everyday operations. Once the volume of the DR reaches a certain threshold, the effect of the DR events on the distribution and transmission system operations will be hard to ignore. This paper proposes changing the business process of DR scheduling and implementation by integrating DR with distribution grid topology. Study cases using OATI webDistribute show the potential DR effect on distribution grid operations and the distribution grid changing the effectiveness of the DR. These examples illustrate the need of integrating demand response with the distribution grid.
summary: Demand side management and demand response receive increasing attention by research and industry. The research work published so far includes a variety of directions from direct load control or targeted customer interaction to indirect incentive-based control (see @cite_18 for an overview). In order to help balancing demand and supply, demand side management programs require accurate predictions of consumer demand.