Building power demand forecasting using K-nearest neighbours model – practical application in Smart City Demo Aspern project

Valgaev, Oleg; Kupzog, Friederich; Schmeck, Hartmut

doi:10.1049/oap-cired.2017.0419

Cited by 21 publications

(8 citation statements)

References 11 publications

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“…They treat this as primarily a clustering problem where they form clusters of similar load profiles and then use distance functions to determine energy consumption for the future. The authors in [122] also use a cloud based clustering approach, using historical power data, they use K-Means clustering to determine the closest historical records and then combine them to predict energy consumption 24 h in advance. The load forecasting problem has been dealt as a regression by [123] using a SVM and by [124] through an RNN using electricity power data.…”

Section: Smart Energymentioning

confidence: 99%

IoT in Smart Cities: A Survey of Technologies, Practices and Challenges

et al. 2021

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Internet of Things (IoT) is a system that integrates different devices and technologies, removing the necessity of human intervention. This enables the capacity of having smart (or smarter) cities around the world. By hosting different technologies and allowing interactions between them, the internet of things has spearheaded the development of smart city systems for sustainable living, increased comfort and productivity for citizens. The IoT for Smart Cities has many different domains and draws upon various underlying systems for its operation. In this paper, we provide a holistic coverage of the Internet of Things in Smart Cities. We start by discussing the fundamental components that make up the IoT based Smart City landscape followed by the technologies that enable these domains to exist in terms of architectures utilized, networking technologies used as well as the Artificial Algorithms deployed in IoT based Smart City systems. This is then followed up by a review of the most prevalent practices and applications in various Smart City domains. Lastly, the challenges that deployment of IoT systems for smart cities encounter along with mitigation measures.

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Section: Smart Energymentioning

confidence: 99%

IoT in Smart Cities: A Survey of Technologies, Practices and Challenges

et al. 2021

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“…The idea to use the Pareto fronts as a tool to select data in the forecasting process originated from the fact that similar, well-known and described in many articles [25][26][27] machine learning algorithm, k nearest neighbors (kNN), has been successfully applied to that task. This algorithm has been used and described in the literature both as a classification algorithm [28,29] and as a forecasting model.…”

Section: The Idea Behind the Pareto Fronts Usagementioning

confidence: 99%

Usage of the Pareto Fronts as a Tool to Select Data in the Forecasting Process—A Short-Term Electric Energy Demand Forecasting Case

Sabat

Baczyński

2021

Energies

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Transmission, distribution, and micro-grid system operators are struggling with the increasing number of renewables and the changing nature of energy demand. This necessitates the use of prognostic methods based on ever shorter time series. This study depicted an attempt to develop an appropriate method by introducing a novel forecasting model based on the idea to use the Pareto fronts as a tool to select data in the forecasting process. The proposed model was implemented to forecast short-term electric energy demand in Poland using historical hourly demand values from Polish TSO. The study rather intended on implementing the range of different approaches—scenarios of Pareto fronts usage than on a complex evaluation of the obtained results. However, performance of proposed models was compared with a few benchmark forecasting models, including naïve approach, SARIMAX, kNN, and regression. For two scenarios, it has outperformed all other models by minimum 7.7%.

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“…Some of these models are developed for specific application areas such as building performance measurement and verification [14][15][16][17], building control [18][19][20] and demand-side management [21,22], whereas a significant number of studies are application agnostic. Literature demonstrates the capability of supervised ML algorithms such as artificial neural networks (ANN) [23], support vector machines (SVM) [24], decision trees [25,26], Gaussian processes [27][28][29] and nearest neighbours [30], among others, in developing reliable building load forecast models. In contrast to the physics based models, the ML based load forecast models require lesser amount of information from the buildings.…”

Section: Literature Reviewmentioning

confidence: 99%

A Machine Learning Pipeline for Demand Response Capacity Scheduling

Krishnadas¹,

Kiprakis

2020

Energies

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Demand response (DR) is an integral component of smart grid operations that offers the necessary flexibility to support its decarbonisation. In incentive-based DR programs, deviations from the scheduled DR capacity affect the grid’s energy balance and result in revenue losses for the DR participants. This issue aggravates with increasing DR delivery from participants such as large consumer buildings who have limited standard methods to follow for DR capacity scheduling. Load curtailment based DR capacity availability from such consumers can be forecasted reliably with the help of supervised machine learning (ML) models. This study demonstrates the development of data-driven ML based total and flexible load forecast models for a retail building. The ML model development tasks such as data pre-processing, training-testing dataset preparation, cross-validation, algorithm selection, hyperparameter optimisation, feature ranking, model selection and model evaluation are guided by deployment-centric design criteria such as reliability, computational efficiency and scalability. Based on the selected performance metrics, the day-ahead and week-ahead ML based load forecast models developed for the retail building are shown to outperform the timeseries persistence models used for benchmarking. Furthermore, the deployment of these models for DR capacity scheduling is proposed as an ML pipeline that can be realised with the help of ML workflows, computational resources as well as systems for monitoring and visualisation. The ML pipeline ensures faster, cost-effective and large-scale deployment of forecast models that support reliable DR capacity scheduling without affecting the grid’s energy balance. Minimisation of revenue losses encourages increased DR participation from large consumer buildings, ensuring further flexibility in the smart grid.

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Building power demand forecasting using K-nearest neighbours model – practical application in Smart City Demo Aspern project

Cited by 21 publications

References 11 publications

IoT in Smart Cities: A Survey of Technologies, Practices and Challenges

IoT in Smart Cities: A Survey of Technologies, Practices and Challenges

Usage of the Pareto Fronts as a Tool to Select Data in the Forecasting Process—A Short-Term Electric Energy Demand Forecasting Case

A Machine Learning Pipeline for Demand Response Capacity Scheduling

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