A computational intelligence approach for residential home energy management considering reward incentives

Ni, Zhen; Paudyal, Priti; Zhong, Xiangnan

doi:10.1109/ssci.2017.8285256

Cited by 5 publications

(2 citation statements)

References 16 publications

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“…The problem is that general purpose software used to design those scenarios is missing for research purposes and to monitor devices as a householder. In general, current solutions focus on algorithms and techniques that can be implemented to optimize consumption while maintaining the comfort of the resident [4], that make efficient use of electrical energy [5], device classification for efficient use [6], and that include climatic and seasonal factors to optimize consumption [7]. Moreover, it is perfectly understandable that a greater effort is being devoted to optimization techniques for electricity consumption since, according to the International Energy Agency (IAEA), global energy demand is 8.3% higher than in 2000, and it is estimated that demand will increase by 2.1% annually until 2040.…”

Section: Introductionmentioning

confidence: 99%

“…To interpret what it means we must go into the details of each group of questions. In the first instance, the first block of questions (1)(2)(3)(4)(5)(6)(7)(8) analyzes the quality of the system in the functional sense, "It solves or not my needs and I feel satisfied", from any point of view, globally or by a particular group of users; this is the criterion that has the 5.8, so the functionality of the system is quite acceptable. The second block of questions refers to the quality of messages, texts, and all kinds of written language present on the platform.…”

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confidence: 99%

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Methodology for Designing an Electricity Demand System in the Context of IoT Household

et al. 2022

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Many efforts have been made to create scenarios whereby interconnecting IoT can be used. The primary objective of these efforts has been to centralize its access as a single list and to monitor its use and trigger its different functionalities from apps. However, few efforts have addressed the problem of electricity consumption from those devices in the context of a residence. Existing datasets for machine predictive systems are focused on data analytics for global consumption but neglect the use of such solutions by the common citizen as a means of re-educating our citizens and optimizing electricity consumption. Without considering the environmental impact and the urgent need to address this growing global emergency, ordinary citizens require systems that help them be aware of what they consume and thus aspire to make a change. In this work, we propose a methodology that builds on the formal mathematical modeling and development of a simulator to substitute the need to collect real data from real world context of use, as well as an interactive system that integrates the whole process. By adding this module to the architecture our prior work, this work is ready for use in real-life scenarios where electrical consumption could be significantly reduced.

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