Short-term smart learning electrical load prediction algorithm for home energy management systems

El-Baz, Wessam; Tzscheutschler, Peter

doi:10.1016/j.apenergy.2015.01.122

Cited by 72 publications

(37 citation statements)

References 36 publications

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“…The operational control of the energy management within such multi-player systems is a challenging task with various degrees of freedom. Coordinated [205] and non-coordinated [206,207] methods have been proposed to cope with the control challenges. Others have proposed and simulated combined control methods (local, distributed and central control) for voltage and load manaement in a distribution network [208].…”

Section: Dispatch Of Bessmentioning

confidence: 99%

Lithium-Ion Battery Storage for the Grid—A Review of Stationary Battery Storage System Design Tailored for Applications in Modern Power Grids

et al. 2017

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Battery energy storage systems have gained increasing interest for serving grid support in various application tasks. In particular, systems based on lithium-ion batteries have evolved rapidly with a wide range of cell technologies and system architectures available on the market. On the application side, different tasks for storage deployment demand distinct properties of the storage system. This review aims to serve as a guideline for best choice of battery technology, system design and operation for lithium-ion based storage systems to match a specific system application. Starting with an overview to lithium-ion battery technologies and their characteristics with respect to performance and aging, the storage system design is analyzed in detail based on an evaluation of real-world projects. Typical storage system applications are grouped and classified with respect to the challenges posed to the battery system. Publicly available modeling tools for technical and economic analysis are presented. A brief analysis of optimization approaches aims to point out challenges and potential solution techniques for system sizing, positioning and dispatch operation. For all areas reviewed herein, expected improvements and possible future developments are highlighted. In order to extract the full potential of stationary battery storage systems and to enable increased profitability of systems, future research should aim to a holistic system level approach combining not only performance tuning on a battery cell level and careful analysis of the application requirements, but also consider a proper selection of storage sub-components as well as an optimized system operation strategy.

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Section: Dispatch Of Bessmentioning

confidence: 99%

Lithium-Ion Battery Storage for the Grid—A Review of Stationary Battery Storage System Design Tailored for Applications in Modern Power Grids

et al. 2017

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“…In our work we show also the effects of aggregation but on a smaller scale, several devices within a household aggregated to the total consumption. Short-term smart learning electrical load prediction algorithm for home energy management systems [11] shows a method which creates first a dayahead forecast and then incrementally adapts the prediction as new data comes in. Altough the scenario they imagined was similar to ours, the dataset is derived from a whole household.…”

Section: Performance Evaluationmentioning

confidence: 99%

Poster Abstract: Real-time load prediction with high velocity smart home data stream

Doblander¹,

Strohbach

Ziekow

et al. 2017

Comput Sci Res Dev

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This paper addresses the use of smart-home sensor streams for continuous prediction of energy loads of individual households which participate as an agent in local markets. We introduces a new device level energy consumption dataset recorded over three years wich includes high resolution energy measurements from electrical devices collected within a pilot program. Using data from that pilot, we analyze the applicability of various machine learning mechanisms for continuous load prediction. Specifically, we address short-term load prediction that is required for load balancing in electrical micro-grids. We report on the prediction performance and the computational requirements of a broad range of prediction mechanisms. Furthermore we present an architecture and experimenal evaluation when this prediction is applied in the stream.

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“…Similarly to price prediction, consumption and demand prediction has been carried out using different machine learning approaches including NN [18], SVR [7], and clustering models [19]. NN and SV-based models appear to be the dominant approaches in consumption prediction; they have been reviewed in the work of Ahmad et al [20].…”

Section: Related Workmentioning

confidence: 99%

Energy cost forecasting for event venues

Zagar

Grolinger

Capretz

et al. 2015

2015 IEEE Electrical Power and Energy Conference (EPEC)

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Abstract-Electricity price, consumption, and demand forecasting has been a topic of research interest for a long time. The proliferation of smart meters has created new opportunities in energy prediction. This paper investigates energy cost forecasting in the context of entertainment event-organizing venues, which poses significant difficulty due to fluctuations in energy demand and wholesale electricity prices. The objective is to predict the overall cost of energy consumed during an entertainment event. Predictions are carried out separately for each event category and feature selection is used to select the most effective combination of event attributes for each category. Three machine learning approaches are considered: k-nearest neighbor (KNN) regression, support vector regression (SVR) and neural networks (NN). These approaches are evaluated on a case study involving a large event venue in Southern Ontario. In terms of prediction accuracy, KNN regression achieved the lowest average error. Error rates varied greatly among different event categories.

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Short-term smart learning electrical load prediction algorithm for home energy management systems

Cited by 72 publications

References 36 publications

Lithium-Ion Battery Storage for the Grid—A Review of Stationary Battery Storage System Design Tailored for Applications in Modern Power Grids

Lithium-Ion Battery Storage for the Grid—A Review of Stationary Battery Storage System Design Tailored for Applications in Modern Power Grids

Poster Abstract: Real-time load prediction with high velocity smart home data stream

Energy cost forecasting for event venues

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