Scalable Clustering of Individual Electrical Curves for Profiling and Bottom-up Forecasting

Auder, Benjamin; Cugliari, Jairo; Goude, Yannig; Poggi, Jean‐Michel

doi:10.20944/preprints201807.0019.v1

Cited by 5 publications

(1 citation statement)

References 30 publications

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“…A substantial forecasting improvement is reached but a long dataset (2 to 3 years) is needed and the algorithm is computationally intensive. A scalable variant of such a strategy is provided in the work of Auder et al…”

Section: Introductionmentioning

confidence: 99%

Clustering electricity consumers using high‐dimensional regression mixture models

Devijver

Goude

Poggi

2019

Appl Stoch Models Bus & Ind

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A massive amount of data about individual electrical consumptions are now provided with new metering technologies and smart grids. These new data are especially useful for load profiling and load modeling at different scales of the electrical network. A new methodology based on mixture of high‐dimensional regression models is used to perform clustering of individual customers. It leads to uncovering clusters corresponding to different regression models. Temporal information is incorporated in order to prepare the next step, the fit of a forecasting model in each cluster. Only the electrical signal is involved, slicing the electrical signal into consecutive curves to consider it as a discrete time series of curves. Interpretation of the models is given on a real smart meter dataset of Irish customers.

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Section: Introductionmentioning

confidence: 99%

Clustering electricity consumers using high‐dimensional regression mixture models

Devijver

Goude

Poggi

2019

Appl Stoch Models Bus & Ind

Self Cite

View full text Add to dashboard Cite

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Deep Learning Based on Multi-Decomposition for Short-Term Load Forecasting

et al. 2018

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Load forecasting is a key issue for efficient real-time energy management in smart grids. To control the load using demand side management accurately, load forecasting should be predicted in the short term. With the advent of advanced measuring infrastructure, it is possible to measure energy consumption at sampling rates up to every 5 min and analyze the load profile of small-scale energy groups, such as individual buildings. This paper presents applications of deep learning using feature decomposition for improving the accuracy of load forecasting. The load profile is decomposed into a weekly load profile and then decomposed into intrinsic mode functions by variational mode decomposition to capture periodic features. Then, a long short-term memory network model is trained by three-dimensional input data with three-step regularization. Finally, the prediction results of all intrinsic mode functions are combined with advanced measuring infrastructure measured in the previous steps to determine an aggregated output for load forecasting. The results are validated by applications to real-world data from smart buildings, and the performance of the proposed approach is assessed by comparing the predicted results with those of conventional methods, nonlinear autoregressive networks with exogenous inputs, and long short-term memory network-based feature decomposition.

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Forecasting in Blockchain-Based Local Energy Markets

Kostmann

Härdle

2019

Energies

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Increasingly volatile and distributed energy production challenges traditional mechanisms to manage grid loads and price energy. Local energy markets (LEMs) may be a response to those challenges as they can balance energy production and consumption locally and may lower energy costs for consumers. Blockchain-based LEMs provide a decentralized market to local energy consumer and prosumers. They implement a market mechanism in the form of a smart contract without the need for a central authority coordinating the market. Recently proposed blockchain-based LEMs use auction designs to match future demand and supply. Thus, such blockchain-based LEMs rely on accurate short-term forecasts of individual households' energy consumption and production. Often, such accurate forecasts are simply assumed to be given. The present research tested this assumption by first evaluating the forecast accuracy achievable with state-of-the-art energy forecasting techniques for individual households and then, assessing the effect of prediction errors on market outcomes in three different supply scenarios. The evaluation showed that, although a LASSO regression model is capable of achieving reasonably low forecasting errors, the costly settlement of prediction errors can offset and even surpass the savings brought to consumers by a blockchain-based LEM. This shows that, due to prediction errors, participation in LEMs may be uneconomical for consumers, and thus, has to be taken into consideration for pricing mechanisms in blockchain-based LEMs.

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Scalable Clustering of Individual Electrical Curves for Profiling and Bottom-up Forecasting

Cited by 5 publications

References 30 publications

Clustering electricity consumers using high‐dimensional regression mixture models

Clustering electricity consumers using high‐dimensional regression mixture models

Deep Learning Based on Multi-Decomposition for Short-Term Load Forecasting

Forecasting in Blockchain-Based Local Energy Markets

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