Customer Baseline Load Estimation for Incentive-Based Demand Response Using Long Short-Term Memory Recurrent Neural Network

Oyedokun, James; Bu, Shengrong; Han, Zhu; Li, Xue

doi:10.1109/isgteurope.2019.8905582

Cited by 7 publications

(4 citation statements)

References 15 publications

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“…The use of artificial neural networks (ANNs) is also one very commonly used method as studied in [24], that offers the capability to capture intricate relationships and patterns within load data, making them effective for handling complex and non-linear load behaviour. They can incorporate multiple influencing factors simultaneously and adapt to changing conditions, improving forecasting accuracy.…”

Section: Proposed Methodology Of Energy Flexibility Assessmentmentioning

confidence: 99%

A Machine Learning Application for the Energy Flexibility Assessment of a Distribution Network for Consumers

Rober,

Maruša,

Beković

2023

Energies

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This paper presents a step-by-step approach to assess the energy flexibility potential of residential consumers to manage congestion in the distribution network. A case study is presented where a selected transformer station exhibits signs of overloading. An analysis has been performed to evaluate the magnitude of the overloading and the timing of the overload occurrence based on their historical load data. Based on the historical load data, the four most prominent consumers have been chosen for the flexibility assessment. Temperature load dependency has been evaluated for the selected consumers. The paper’s novel approach focuses on selecting individual consumers with the highest energy flexibility potential, and analysing their load patterns to address transformer overloading. To achieve this, machine learning algorithms, specifically, multiple linear regression and support vector machines, were used for load profile forecasting during the overload occurrences. Based on the forecast and measured load patterns, flexibility scenarios were created for each consumer. The generated models were evaluated and compared with the forecasting based on the average load of the past days. In the results, three potential consumers were identified who could resolve the transformer overloading problem. The machine learning models outperformed the average-based forecasting method, providing more realistic estimates of flexibility potential. The proposed approach can be applied to other overloaded transformer stations, but with a limited number of consumers.

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Section: Proposed Methodology Of Energy Flexibility Assessmentmentioning

confidence: 99%

A Machine Learning Application for the Energy Flexibility Assessment of a Distribution Network for Consumers

Rober,

Maruša,

Beković

2023

Energies

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“…To handle highly irregular and volatile load profiles of residential customers, deep learning-based approaches are proposed. In [15], [16], the authors utilize long short-term memory (LSTM), which can effectively deal with time series data such as electricity usage data. In [17], the authors utilize the reconstruction capability of staked Autoencoder.…”

Section: ) Deep Learning Methodsmentioning

confidence: 99%

“…Most of the existing work focuses on CBL calculation methods for residential customers and operation strategies for DR operators. The CBL calculation methods include direct CBL calculation such as averaging methods [9], regression [13], [14], deep learning [15]- [21], and probabilistic methods [7], [22]- [25] and indirect CBL calculation methods such as control group methods [24], [26]- [35]. The operation strategies for DR operators include optimal bidding strategy [38]- [47] and modeling of residential customers [8], [23], [48]- [53].…”

Section: Introductionmentioning

confidence: 99%

Novel Single Group-Based Indirect Customer Baseline Load Calculation Method for Residential Demand Response

Lee

Jang²,

et al. 2021

IEEE Access

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Demand response (DR) is a voluntary program that encourages related stakeholders, in this case electricity consumers, to cut down on usage during periods of high electricity load. One key to fully exploiting DR is to encourage residential customers to join the DR program. Unlike in the DR programs for commercial and industrial customers, for the successful operation of the residential DR program, several issues have to be addressed, one of which is to provide a group-level incentive to participating customers. In particular, the issue comes up when the incentive calculated for a group is not equal to the aggregated incentives for each customer (i.e., non-equal incentive problem). The non-equal incentive problem deteriorates the successful operation of residential DR by decreasing the motivation of DR operators and customers. We first prove the non-equal incentive problem through mathematical and experimental methods. We then propose the novel single group-based indirect incentive calculation method. The basic idea of our approach is to indirectly calculate the incentive for each customer not using the customer's data but using the data of other customers of the same DR group. Through experiments involving the electricity usage data of 42,193 households and the real DR events in Korea, we show that our method solves the non-equal incentive problem in most cases. Furthermore, our method improves the accuracy of the baseline estimation (used for calculating the contribution).

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“…In view of these results, application of ML techniques has been sought in the estimation of BLP for DR. In a comparative study of five ML techniques-high X of Y, last Y days, regression, neural network, and polynomial interpolation-carried out on actual smart meter data, NN and polynomial interpolation outperformed the other methodologies in terms of reliability of prediction, lowering the estimation error [107,108]. NN has also shown useful application in BL calculation for industrial facilities [109].…”

Section: Novel Tools For Estimationmentioning

confidence: 99%

Demand Response Impact Evaluation: A Review of Methods for Estimating the Customer Baseline Load

et al. 2022

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Climate neutrality is one of the greatest challenges of our century, and a decarbonised energy system is a key step towards this goal. To this end, the electricity system is expected to become more interconnected, digitalised, and flexible by engaging consumers both through microgeneration and through demand side flexibility. A successful use of these flexibility tools depends widely on the evaluation of their effects, hence the definition of methods to assess and evaluate them is essential for their implementation. In order to enable a reliable assessment of the benefits from participating in demand response, it is necessary to define a reference value (“baseline”) to allow for a fair comparison. Different methodologies have been investigated, developed, and adopted for estimating the customer baseline load. The article presents a structured overview of methods for the estimating the customer baseline load, based on a review of academic literature, existing standardisation efforts, and lessons from use cases. In particular, the article describes and focuses on the different baseline methods applied in some European H2020 projects, showing the results achieved in terms of measurement accuracy and costs in real test cases. The most suitable methodology choice among the several available depends on many factors. Some of them can be the function of the Demand Response (DR) service in the system, the broader regulatory framework for DR participation in wholesale markets, or the DR providers characteristics, and this list is not exclusive. The evaluation shows that the baseline methodology choice presents a trade-off among complexity, accuracy, and cost.

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Customer Baseline Load Estimation for Incentive-Based Demand Response Using Long Short-Term Memory Recurrent Neural Network

Cited by 7 publications

References 15 publications

A Machine Learning Application for the Energy Flexibility Assessment of a Distribution Network for Consumers

A Machine Learning Application for the Energy Flexibility Assessment of a Distribution Network for Consumers

Novel Single Group-Based Indirect Customer Baseline Load Calculation Method for Residential Demand Response

Demand Response Impact Evaluation: A Review of Methods for Estimating the Customer Baseline Load

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