reference 1 
Abstract: ifferentially-private model that perturbs data by adding noise obtained from a virtual chargeable battery, while maintaining billing accuracy.
https://ieeexplore.ieee.org/abstract/document/9756499

reference 2 
Abstract : unsupervised clustering of devices' data to find the states of operation for each device, followed by probabilistically analyzing user behavior to determine their preferred states.
https://arxiv.org/abs/1912.03298

reference 3
Abstract:A total of thirty-one databases are examined and compared in terms of several features, such as the geographical location, period of collection, number of monitored households, sampling rate of collected data, number of sub-metered appliances, extracted features and release date.
https://www.sciencedirect.com/science/article/pii/S037877882030815X

reference 4
Abstarct: Based on an extensive original dataset involving expert interviews, supplemented with a review of the literature, this study elaborates on an array of social, technical, political, and environmental risks facing smart home innovation, with clear implications for research, policy, and technology development. 
https://iopscience.iop.org/article/10.1088/1748-9326/abe90a/meta

reference 5
Abstract: in this paper we develop a deep neural network based model that predicts the temperature in various rooms of the home function of the state of the actuators. We also describe a scaled model of a four room home which allows us to control the doors and windows and collect data using IoT devices.
https://ieeexplore.ieee.org/abstract/document/9013312

reference 6
Abstract: This study provides an overview and analysis of the existing regulation and standards in the UK building/household sector, to understand the current state-of-the-art and identify gaps preventing Green-Tech wider implementation and use
https://www.mdpi.com/2071-1050/14/7/4030


keywords : power consumption, Smart meters, Optimizing Energy, Energy Efficiency, Temperature sensors, green technologies.