Identifying Representative Load Time Series for Load Flow Calculations

Henze, Janosch; Kneiske, T.; Braun, Martin; Sick, Bernhard

doi:10.1007/978-3-319-71643-5_8

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…The relation between the two can be framed as a missing data problem for the less often sampled dataset where specific methods such as spectrum estimation and others can be applied to achieve a correspondence between the two. For the particular context of power system analysis for load flow calculations, the authors of [5] leverage feature extraction to reconstruct synthetically representative time series as a means for reduction of computational demands of the algorithms with bounded modelling quality degradation. Computational intelligence methods such as generative adversarial networks can be used to learn and extrapolate measurement time series patterns as is the case with the TimeGANs for generating quality datasets [6].…”

Section: Related Workmentioning

confidence: 99%

Reporting Interval Impact on Deep Residential Energy Measurement Prediction

Stâmâtescu

Ciornei

Plamanescu

et al. 2021

2021 IEEE 11th International Workshop on Applied Measurements for Power Systems (AMPS)

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Forecasting and anomaly detection for energy time series is emerging as an important application area for computational intelligence and learning algorithms. The training of robust data-driven models relies on large measurement datasets sampled at ever increasing rates and demand large computational and storage resources for off-line power quality analysis and on-line control in energy management schemes. We analyze the impact of the reporting rate of energy measurements on deep learning based forecasting models in a residential scenario. The work is also motivated by the development of embedded energy gateways for online inference and anomaly detection that avoids the dependence on costly, high-latency, cloud systems for data storage and algorithm evaluation. This in turn requires increased local computation and memory requirements to generate predictions within the control sampling period. We report quantitative forecasting metrics (MSE, MAE, MAPE) to establish an empirical trade-off between reporting rate and model accuracy. Additional results consider the rate-variable feature extraction using a time series data mining algorithm for multi-scale analytics.

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Section: Related Workmentioning

confidence: 99%

Reporting Interval Impact on Deep Residential Energy Measurement Prediction

Stâmâtescu

Ciornei

Plamanescu

et al. 2021

2021 IEEE 11th International Workshop on Applied Measurements for Power Systems (AMPS)

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“…For the investigations presented in this paper, a method focussing on a systematic choice and combination of daily profiles from high-resolution long-term measurements is pursued. With means of these test profiles, important statistical characteristics of the input data must be maintained [41]. A couple of publications on this topic, especially concerning grid stability issues…”

Section: Appendix a Derivation Of Test Profiles For Application Testsmentioning

confidence: 99%

Comparison of Performance-Assessment Methods for Residential PV Battery Systems

Niedermeyer

Braun

2020

Energies

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Declining costs for high-performance batteries are leading to a global increased use of storage systems in residential buildings. Especially in conjunction with reduced photovoltaic (PV) feed-in tariffs, a large market has been developed for PV battery systems to increase self-sufficiency. They differ in the type of coupling between PV and battery, the nominal capacities of their components, and their degree of integration. High system performance is particularly important to achieve profitability for the operator. This paper presents and evaluates methods for a uniform determination of PV battery system performance. Already the requirement analysis reveals that a performance comparison of PV battery systems must cover the efficiency and effectiveness during system operation. A method based on a derivation of key performance indicators (KPIs) for these two criteria through an application test is proposed. It is evaluated by comparison to other methods, such as the System Performance Index (SPI) and aggregation of conversion and storage efficiency. These methods are applied with five systems in a laboratory test bench to identify their advantages and drawbacks. Here, a particular focus is on compliance with the initially formulated requirements in terms of both test procedures and KPI derivations. Analysis revealed that the proposed method addresses these requirements well, and is beneficial in terms of result comprehensibility and KPI validity.

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