Data Mining zur Analyse der Auswirkungen von Preissignalen auf Haushaltsstromkunden

Waczowicz, Simon; Klaiber, Stefan; Bretschneider, Peter; Konotop, Irina; Westermann, Dirk; Reischl, Markus; Mikut, Ralf

doi:10.1515/auto-2014-1108

Cited by 10 publications

(4 citation statements)

References 16 publications

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“…Третья группа состоит из модулей облачных вычислений, реализующих модели объекта, алгоритмы адаптации, программного управления и предиктивной аналитики [13]. Контроллер IoT осуществляет непрерывное регулирование технологического параметра по классическому алгоритму регулирования по обратной связи в соответствии с заданием, обеспечивая удовлетворительное состояние объекта при отсутствии связи с облачным сервером.…”

Section: материалы и методыunclassified

Compensation predictive automation of the smart house climate control systems

et al. 2019

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Introduction. The gained experience in the field of building automation and IoT technologies yields a new approach to the management of engineering subsystems that provides stated parameters of operation quality throughout the entire building lifecycle. This paper explores compensatory and predictive algorithms in the scope of the aforementioned approach to manifest control over building climate parameters utilizing IoT controllers. This research aims to improve the management efficiency of smart house engineering subsystems through the implementation of a control system (CS) capable to compensate disturbances and predict their variations using an IoT controller and an analytical server. Materials and methods. In order to improve the quality of control, various algorithms based on analysis of data collected from controllers can be employed. The collected data about the object accumulated over the entire period of operation can be used to build a model for the purposes of predictive control. The predictive control allows to forecast the parameters having an effect on the object and compensate it beforehand under the inertia conditions. The continuous adaptation and adjustment of the CS model to operating conditions allows permanent optimizing the settings of the control algorithm ensuring the efficient operation of local control loops. Results. The CS is based on an IoT controller and able to predict and compensate potential disturbances. The compensation algorithm is updated depending on the behavior of the object properties, quality of control and availability of data most suitable for identification. Conclusions. The capabilities of the control system based on the IoT controller and generation of a compensatory and predictive control signal with the algorithm hosted at a cloud server are demonstrated on the indoor temperature control model. The following simulation models of the indoor temperature variation process are considered: model without CS, model with proportional plus integral controller with disturbance compensation and model with IoT controller-based CS with disturbance compensation. Structural and parametric identification of the models are accomplished by means of active experiment.

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Section: материалы и методыunclassified

Compensation predictive automation of the smart house climate control systems

et al. 2019

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“…1). The workflow is shared amongst them and distributed as follows: ‘Planning process’ denotes the blocks for calculating the OPF and cost‐optimisations. ‘Forecast system’ are the blocks responsible for the consumer behaviour modelling (in [19]) and the aggregation of household busses. ‘Influenced consumer process’ – for predictive modelling (in [14]). …”

Section: Closed‐loop System Modelmentioning

confidence: 99%

“…The uninfluenced load component P U [ t ] as a result of the consumer process is independent of the price elasticity. The proposed approach is developed by ‘Influenced consumer process’ consumer models [14].…”

Section: Closed‐loop System Modelmentioning

confidence: 99%

“…Further analysis aims at a very granular level, as transactive control of the individual end‐devices [12, 13] in real time. Finally, data mining is essential in forecasting the electricity consumption and predicting probable changes in the consumption patterns as response to the price change, addressed in prior studies [14, 15]. However, the networks calculations were not paid for much attention so far.…”

Section: Introductionmentioning

confidence: 99%

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Investigation and mathematical modelling of the impact of incentive signals to consumers on their consumption, load forecast and network operation

Konotop¹,

Waczowicz

Klaiber

et al. 2016

IET Generation, Transmission & Distribution

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Due to increasing contribution of renewable energy sources to the overall energy production, future control of energy consumption should achieve the principle ‘generation following demand’. Within this approach, also known as demand side management, consumers are prompted to alter demands through an incentive signal. This work addresses the use of variable price as such incentive signal to influence power consumption of private households in a controllable manner. The mathematical modelling is based on simulations of optimal power flow and includes several optimisation constraints and criteria, according to which both direct and inverse models are constructed. Therefore, the problem of power signals and price signals is considered as fully-coupled. The analysis is performed for a model, representing typical conditions and scenarios of a residential network with household customers, including solar and wind power plants. The optimisation criteria are constructed with the emphasis on distribution system operators. The results of computations, performed with simplified and extended optimisation models, show a pronounced effect on the overall power consumption, reduction of the extreme values and minimisation of the peaks. Finally, further steps are proposed to extend the model to target various optimisation constraints

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Information and Communication Technology in Energy Lab 2.0: Smart Energies System Simulation and Control Center with an Open‐Street‐Map‐Based Power Flow Simulation Example

et al. 2016

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In Energy Lab 2.0, the interplay of different forms of energy on different value chains is investigated. Novel concepts to stabilize the volatile energy supply of renewables by the use of storage systems and mainly by applying to‐be‐developed tools and algorithms of the information and communication technology sector are sought. Hence, a key element of Energy Lab 2.0 is the smart energies system simulation and control center. This consists of three parts: a power‐hardware‐in‐the‐loop experimental field, an energy grid simulation and analysis laboratory, and a control, monitoring, and visualization center. For these three labs, big data technologies, advanced control methods, and reliable, safe, and secure software structures are of equal importance. As an example, a data processing pipeline to create power flow simulation models from raw Open Street Map data, statistical databases, and geodata is presented and discussed.

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Data Mining zur Analyse der Auswirkungen von Preissignalen auf Haushaltsstromkunden

Cited by 10 publications

References 16 publications

Compensation predictive automation of the smart house climate control systems

Compensation predictive automation of the smart house climate control systems

Investigation and mathematical modelling of the impact of incentive signals to consumers on their consumption, load forecast and network operation

Information and Communication Technology in Energy Lab 2.0: Smart Energies System Simulation and Control Center with an Open‐Street‐Map‐Based Power Flow Simulation Example

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