arXiv subjects

Daniel R. Bayer

Publications and source records attributed to Daniel R. Bayer.

4 recordsLinked to original sources

arXiv2026

Quantification and Surrogate Model Estimation of Spatial-Temporal Carbon Intensity Factors

The transition toward sustainable urban infrastructure is driving a rapid increase in distributed energy resources, particularly among prosumers equipped with solar photovoltaic (PV) installations. Such PV systems are highly dependent on solar radiation, which fluctuates over time and across different city districts, leading to substantial spatial and temporal variations in local carbon intensity factors. The carbon intensity factor is a critical parameter for optimizing energy consumption and reducing emissions in context of smart building control, electric vehicle charging or district heating systems. Hence, high-resolution information on local carbon intensity factors is crucial for intelligent control strategies that minimize environmental impact across urban infrastructures. In this paper, we introduce a novel approach for calculating spatially and temporally resolved carbon intensity factors on an urban district level. The results show that, even within a single city, these factors range from 0 up to 316 g/kWh for an exemplary summer noon. We further develop a transferable surrogate model that estimates these factors using only a limited set of input parameters, typically available to municipalities or grid operators, aligned with census grid cell information. For the surrogate model we compare both traditional decision tree based methods and a state of the art neural network architecture. Our findings demonstrate that the surrogate model can provide highly accurate estimations, particularly in densely built urban areas, supporting data-driven sustainable city operations and indicating transferability even to regions with limited available information. A case study further reveals that neglecting local variations can lead to emission estimation errors of up to 9% for an exemplary building.

eess.SY↗

arXiv2026

Benchmarking Transformer and xLSTM for Time-Series Forecasting of Heat Consumption

Obtaining an accurate short-term forecasting for heat demand is an essential part of operating district heating networks cost-efficient and reliable. Heat consumption time series at the building level are highly dependent on exogenous variables such as outdoor temperature and individual usage patterns, making forecasting in this context a challenging task. Thus, this paper benchmarks novel Transformer-based and xLSTM architectures for short-term heat-demand forecasting. Using hourly data from 25 German buildings (2017-2025), we compare three-hour and 24-hour forecasting horizons relevant for intraday control and day-ahead scheduling. We establish a multi-building benchmark that tests whether models trained on pooled, heterogeneous building data are able to generalize across diverse building stock. The results show that the xLSTM achieves the lowest RMSE (19.88 kWh for three-hour, 21.47 kWh for 24-hour forecasts), while the Temporal Fusion Transformer attains the best MAE (9.16 kWh for three-hour forecasts). As xLSTMs and Transformers require long training times and have a huge number of trainable parameters, their sustainability remains questionable. Therefore, this paper further investigates the trade-off between predictive accuracy and computational resource demand of the evaluated forecasting models. The findings indicate that also low-parameter models like a traditional fully-connected network achieve good predictive results, highlighting that marginal accuracy gains of the novel prediction models come at substantial resource expense for this use case.

cs.LG↗

arXiv2025

Electricity Demand Forecasting in Future Grid States: A Digital Twin-Based Simulation Study

Short-term forecasting of residential electricity demand is an important task for utilities. Yet, many small and medium-sized utilities still use simple forecasting approaches such as Synthesized Load Profiles, which treat residential households similarly and neither account for renewable energy installations nor novel large consumers (e.g., heat pumps, electric vehicles). The effectiveness of such "one-fits-all" approaches in future grid states--where decentral generation and sector coupling increases--are questionable. Our study challenges these forecasting practices and investigates whether Machine Learning (ML) approaches are suited to predict electricity demand in today's and in future grid states. We use real smart meter data from 3,511 households in Germany over 34 months. We extrapolate this data with future grid states (i.e., increased decentral generation and storage) based on a digital twin of a local energy system. Our results show that Long Short-Term Memory (LSTM) approaches outperform SLPs as well as simple benchmark estimators with up to 68.5% lower Root Mean Squared Error for a day-ahead forecast, especially in future grid states. Nevertheless, all prediction approaches perform worse in future grid states. Our findings therefore reinforce the need (a) for utilities and grid operators to employ ML approaches instead of traditional demand prediction methods in future grid states and (b) to prepare current ML methods for future grid states.

cs.CY↗

arXiv2023

Modeling of Annual and Daily Electricity Demand of Retrofitted Heat Pumps based on Gas Smart Meter Data

Currently, gas furnaces are common heating systems in Europe. Due to the efforts for decarbonizing the complete energy sector, heat pumps should continuously replace existing gas furnaces. At the same time, the electrification of the heating sector represents a significant challenge for the power grids and their operators. Thus, new approaches are required to estimate the additional electricity demand to operate heat pumps. The electricity required by a heat pump to produce a given amount of heat depends on the Seasonal Performance Factor (SPF), which is hard to model in theory due to many influencing factors and hard to measure in reality as the heat produced by a heat pump is usually not measured. Therefore, we show in this paper that collected smart meter data forms an excellent data basis on building level for modeling heat demand and the SPF. We present a novel methodology to estimate the mean SPF based on an unpaired dataset of heat pump electricity and gas consumption data taken from buildings within the same city by comparing the distributions using the Jensen-Shannon Divergence (JSD). Based on a real-world dataset, we evaluate this novel method by predicting the electricity demand required if all gas furnaces in a city were replaced by heat pumps and briefly highlight possible use cases.

cs.CY↗