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Probabilistic Short-Term Low-Voltage Load Forecasting using Bernstein-Polynomial Normalizing Flows

The transition to a fully renewable energy grid requires better forecasting of demand at the low-voltage level. However, high fluctuations and increasing electrification cause huge forecast errors with traditional point estimates. Probabilistic load …

Integration of Building Inertia Thermal Energy Storage into Smart Grid Control

A building's structural mass does provide inherent thermal storage capabilities. Within this work, a mathematical model of a building inertia thermal energy storage (BITES) is proposed to allow integration into optimized smart grid control for real-world applications.

Sector-Coupled District Energy Management with Heating and Bi-Directional EV-Charging

With the decentralization of the energy supply, district energy management with sector-coupling gains importance, as we urge towards a fast reduction of greenhouse gas emissions and aim at more efficient energy usage. By jointly optimizing and …

Permutation-Based Residential Short-term Load Forecasting in the Context of Energy Management Optimization Objectives

What makes a household-level short-term load forecast "good"? Individual household load profiles are intermittent, as distinct peaks correspond to specific activities in the household. Using traditional point-wise error metrics to assess …

Short-term probabilistic load forecasting at low aggregation levels using convolutional neural networks

Lowly aggregated load profiles such as of individual households or buildings are more fluctuating and relative forecast errors are comparatively high. Therefore, the prevalent point forecasts are not sufficiently capable of optimally capturing …

Subgradient Methods for Averaging Household Load Profiles under Local Permutations

This paper introduces an approximation of the permutation invariant (LPI) sample mean to average smart meter load profiles, with applications in load forecasting and clustering.

Adjusted Feature-Aware k-Nearest Neighbors: Utilizing Local Permutation-Based Error for Short-Term Residential Building Load Forecasting

Household load profiles are more fluctuating than higher aggregated load profiles and relative forecast errors are comparatively high. To handle this, adjusted error metric and average concepts have been proposed to be used to obtain more suitable …

Residential short-term load forecasting using convolutional neural networks

Low aggregations of electric load profiles are more fluctuating, relative forecast errors are comparatively high, and it has been shown that different forecast models and feature configurations may be best suitable for specific households or …

The Impact of Electric Vehicles on Low Voltage Grids: A Case Study of Berlin

Replacing fossil-fueled vehicles with electric vehicles (EVs) poses new challenges for public distribution networks. In order to accommodate these new loads in current grid extension planning, distribution system operators (DSOs) have to consider …