Multi-kernel Analysis Paradigm Implementing the Learning from Loads Approach for Smart Power Systems

Alamaniotis, Miltiadis

doi:10.1007/978-3-030-15628-2_5

Cited by 3 publications

(2 citation statements)

References 29 publications

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“…This collection of information opens opportunities for a given individual to have better insight into their consumption patterns, make adjustments that will reduce unnecessary load draws, or even detect anomalies in their household's power usage [3]. This information can also be made accessible to power grid utilities, enabling them to offer tailored demand response and management programs that reduce or shift energy consumption; additionally, this information can aid power grid utilities in securing grid operations [4].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Room-Based Identification of Electricity Consumption with an Ensemble Learning Method in Smart Energy

Ramirez

Alamaniotis

2021

Energies

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This paper frames itself in the realm of smart energy technologies that can be utilized to satisfy the electricity demand of consumers. In this environment, demand response programs and the intelligent management of energy consumption that are offered by utility providers will play a significant role in implementing smart energy. One of the approaches to implementing smart energy is to analyze consumption data and provide targeted contracts to consumers based on their individual consumption characteristics. To that end, the identification of individual consumption features is important for suppliers and utilities. Given the complexity of smart home load profiles, an appliance-based identification is nearly impossible. In this paper, we propose a different approach by grouping appliances based on their rooms; thus, we provide a room-based identification of energy consumption. To this end, this paper presents and tests an intelligent consumption identification methodology, that can be implemented in the form of an ensemble of artificial intelligence tools. The ensemble, which comprises four convolutional neural networks (CNNs) and four k-nearest neighbor (KNN) algorithms, is fed with smart submeter data and outputs the identified type of room in a given dwelling. Results obtained from real-world data exhibit the superiority of the ensemble, with respect to accuracy, as compared with individual CNN and KNN models.

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Section: Introductionmentioning

confidence: 99%

Intelligent Room-Based Identification of Electricity Consumption with an Ensemble Learning Method in Smart Energy

Ramirez

Alamaniotis

2021

Energies

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“…In addition, it assembles the kernel machines into a deep neural network architecture that is called neuro-kernel-machine network (NKMN)). The goal of the NKMN is to analyze the historical data aiming at capturing the energy consumption behavior of the citizens by using a set of kernel machines-with each machine to model a different set of data properties- [2]. Then, the kernel machines interact via a deep neural network that accommodates the interconnection of kernel machines via a set of weights.…”

Section: Introductionmentioning

confidence: 99%

Neuro-Kernel-Machine Network Utilizing Deep Learning and Its Application in Predictive Analytics in Smart City Energy Consumption

Alamaniotis

2020

Advances in Data Science: Methodologies and Applications

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In the smart cities of the future artificial intelligence (AI) will have a dominant role given that AI will accommodate the utilization of intelligent analytics for prediction of critical parameters pertaining to city operation. In this chapter, a new data analytics paradigm is presented and being applied for energy demand forecasting in smart cities. In particular, the presented paradigm integrates a group of kernel machines by utilizing a deep architecture. The goal of the deep architecture is to exploit the strong capabilities of deep learning utilizing various abstraction levels and subsequently identify patterns of interest in the data. In particular, a deep feedforward deep neural network is employed with every network node to implement a kernel machine. This deep architecture, named neuro-kernel machine network, is subsequently applied for predicting the energy consumption of groups of residents in smart cities. Obtained results exhibit the capability of the presented method to provide adequately accurate predictions despite the form of the energy consumption data.

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Multi-kernel Analysis Method for Intelligent Data Processing with Application to Prediction Making

Alamaniotis

2022

Smart Innovation, Systems and Technologies

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Multi-kernel Analysis Paradigm Implementing the Learning from Loads Approach for Smart Power Systems

Cited by 3 publications

References 29 publications

Intelligent Room-Based Identification of Electricity Consumption with an Ensemble Learning Method in Smart Energy

Intelligent Room-Based Identification of Electricity Consumption with an Ensemble Learning Method in Smart Energy

Neuro-Kernel-Machine Network Utilizing Deep Learning and Its Application in Predictive Analytics in Smart City Energy Consumption

Multi-kernel Analysis Method for Intelligent Data Processing with Application to Prediction Making

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