ACM SIGBED Review

Shenoy, Prashant

doi: 10.1145/3607120.3607121pmid: N/A

As our society undertakes an energy transition to low carbon future, many sectors ranging from the energy grid to transportation are in the midst of a transformation to newer and cleaner technologies such as electric vehicles, electric heat pumps, and distributed renewables. These technologies will have a direct impact on an end-users in terms of behavior, comfort, and lifestyle. In this column, we examine the user's perspective of an energy transition.

Chadoulos, Spiros; Koutsopoulos, Iordanis; Polyzos, George C.

doi: 10.1145/3607120.3607122pmid: N/A

Forecasting the energy demand of individual consumers is a vital component of future smart energy grids since it enables energy-saving mechanisms such as Demand Response, activity scheduling, and prosumer energy markets. However, training a separate model with each consumer's available smart meter data can raise significant cold-start and scalability issues, despite the fact that personalization can be achieved in cases where the respective training sets have adequate data. Namely, making accurate forecasts for new consumers with limited historical data is challenging since a machine learning model requires a significant volume of data to be trained adequately, while scalability becomes an issue when the number of consumers increases. Training a single model on multiple consumers can mitigate these issues, hence we propose a single-model RNN-based deep learning architecture named Deep4Ener, for consumer-level energy demand forecasting, trained on multiple users and capable of making predictions for unseen consumers with scarce historical data that were not included in the training phase. Deep4Ener learns common energy demand characteristics among different consumers, by utilizing a novel architecture for energy profiling, including clustering, and an encoder neural network for feature extraction. Experiments with data from two open datasets show that Deep4Ener achieves high predictive performance both for known and completely new consumers, while outperforming the current state-of-the-art, namely one-model-per-consumer, standalone RNN, and Amazon's DeepAR approaches. Finally, we demonstrate that Deep4Ener shines when combined with Transfer Learning to further improve its forecasting performance on different energy demand consumers with limited data available.

Oest, Frauke; Frost, Emilie; Radtke, Malin; Lehnhoff, Sebastian

doi: 10.1145/3607120.3607123pmid: N/A

The increasing integration of renewable energy resources requires so-called smart grid services for monitoring, control and automation tasks. Simulation environments are vital for evaluating and developing innovative solutions and algorithms. Especially in smart energy systems, we face a variety of heterogeneous simulators representing, e.g., power grids, analysis or control components and markets. The co-simulation framework mosaik can be used to orchestrate the data exchange and time synchronization between individual simulators. So far, the underlying communication infrastructure has often been assumed to be optimal and therefore, the influence of e.g., communication delays has been neglected. This paper presents the first results of the project cosima, which aims at connecting the communication simulator OMNeT++ to the co-simulation framework mosaik to analyze the resilience and robustness of smart grid services, e.g., multi-agent-based services with respect to adaptivity, scalability, extensibility and usability. This facilitates simulations with realistic communication technologies (such as 5G) and the analysis of dynamic communication characteristics by simulating multiple messages. We show the functionality and benefits of cosima in experiments with 50 agents.