Home appliances are considered to account for a large portion of smart homes’ energy consumption. This is due to the abundant use of IoT devices. Various home appliances, such as heaters, dishwashers, and vacuum cleaners, are used every day. It...
Accurate building energy prediction is useful in various applications starting from building energy automation and management to optimal storage control. However, vulnerabilities should be considered when designing building energy prediction models,...
Due to the transition toward the Internet of Everything (IOE), the prediction of energy consumed by household appliances has become a progressively more difficult topic to model. Even with advancements in data analytics and machine learning, several...
Representing and analyzing the complexity of models constructed by data is a difficult and challenging task, hence the need for new, more effective techniques emerges, despite the numerous methodologies recently proposed in this field. In the present...
Rising household electricity consumption, driven by technological advances and increased indoor activity, has led to higher energy costs and an increased reliance on non-renewable sources, exacerbating the carbon footprint. Home energy management sys...
The energy use patterns of electrical appliances are more difficult to predict than energy use for heating, ventilation, and air-conditioning (HVAC) and lighting, as: (1) there are large varieties of electrical equipment (e.g., appliances, vending ma...
From a single meter that measures the entire home’s electrical demand, energy disaggregation calculates appliance-by-appliance electricity consumption. Non-intrusive load monitoring (NILM), also known as energy disaggregation, tries to decompos...
The building sector, responsible for 40% of global energy consumption, faces increasing demands for sustainability and energy efficiency. Accurate energy consumption forecasting is essential to optimise performance and reduce environmental impact. Th...
Energy consumption is increasing daily, and with that comes a continuous increase in energy costs. Predicting future energy consumption and building an effective energy management system for smart homes has become essential for many industrialists to...
In response to the need to improve energy-saving processes in older buildings, especially residential ones, this paper describes the potential of a novel method of disaggregating loads in light of the load patterns of household appliances determined...
In this work, an algorithm for the scheduling of household appliances to reduce the energy cost and the peak-power consumption is proposed. The system architecture of a home energy management system (HEMS) is presented to operate the appliances. The...
Responsible, efficient and environmentally aware energy consumption behavior is becoming a necessity for the reliable modern electricity grid. In this paper, we present an intelligent data mining model to analyze, forecast and visualize energy time s...
Nowadays Off-Grid systems driven by intelligent data analysis, predictive models and real-time optimizations are frequently entitled as Smart Grids. They represent a future step in a sustainable energy supply for applications at various scales, from...
This article presents the analysis of the influence of household appliances on the quality of the energy consumed by the end-user. The results of the research, then, concern the final consumer (the lowest level of the power grid). The research was co...
Accurate energy consumption prediction in commercial buildings is a challenging research task. Energy prediction plays a crucial role in energy efficiency, management, planning, sustainability, risk management, diagnosis, and demand response. Althoug...
Demand for electricity is constantly increasing, and production is facing new constraints due to the current world situation. An alternative to standard energy production methodologies is based on the use of renewable sources; however, these methodol...
Demand response (DR) program can shift peak time load to off-peak time, thereby reducing greenhouse gas emissions and allowing energy conservation. In this study, the home energy management scheduling controller of the residential DR strategy is prop...
Non-Intrusive Load Monitoring (NILM) increases awareness on user energy usage patterns. In this paper, an efficient and highly accurate NILM method is proposed featuring condensed representation, super-state and fusion of two deep learning based mode...
Energy behaviours will play a key role in decarbonising the building sector but require the provision of tailored insights to assist occupants to reduce their energy use. Energy disaggregation has been proposed to provide such information on the appl...
Residential demand-side management (DSM) of electricity has been gaining attention as a way to reduce energy consumption at home and as a way of maximizing the utilization of fluctuating solar power generation. To promote the smooth introduction of D...
With the massive, worldwide, smart metering roll-out, both energy suppliers and users are starting to tap into the potential of higher resolution energy readings for accurate billing, improved demand response, improved tariffs better tuned to users a...
The forecasting of the load profile of the domestic sector is an area of increased concern for the power grid as it appears in many applications, such as grid operations, demand side management, energy trading, and so forth. Accordingly, a bottom-up...
Predicting errors in home appliances is crucial for maintaining the reliability and efficiency of smart homes. However, there is a significant lack of such data on appliance malfunctions that can be used in developing effective anomaly detection mode...
Insightful information on energy use encourages home residents to conduct home energy conservation. This paper proposes an experimental design for an energy disaggregation system based on the low-computational-cost approaches of multi-target classifi...
Since energy consumption in developing countries has increased significantly, motivating energy-saving habits among citizens is an important issue both from the academic and industrial perspectives. Thus, this study aims to predict consumer purchase...
Heating appliances consume approximately 48 % of the energy spent on household appliances every year. Furthermore, a malf...
This paper presents a data-driven approach that leverages reinforcement learning to manage the optimal energy consumption of a smart home with a rooftop solar photovoltaic system, energy storage system, and smart home appliances. Compared to existing...
To ensure reliable, efficient and sustainable operation of modern power networks, accurate load forecasting is an important task in system planning and control. It is also a crucial task for the efficient operation of smart grids to maintain a balanc...
The consumption of household appliances tends to increase. Therefore, the application of energy efficiency measurements is urgently needed to reduce the levels of power consumption. Over the last years, various methods have been used to predict house...
In the quest to improve energy efficiency in residential environments, home energy management systems (HEMSs) have emerged as an effective solution, leveraging artificial intelligence (AI) technologies to improve energy efficiency. This study propose...
Electricity consumption in homes is on the rise due to the increasing prevalence of home appliances and longer hours spent indoors. Home energy management systems (HEMSs) are emerging as a solution to reduce electricity consumption and efficiently ma...
This work proposes a framework to solve demand-side management (DSM) problem by systematically scheduling energy consumption using flat pricing scheme (FPS) in smart grid (SG). The framework includes microgrid with renewable energy sources (solar and...
One of the important aspects of realizing smart cities is developing smart homes/buildings and, from the energy perspective, designing and implementing an efficient smart home area energy management system (HAEMS) is vital. To be effective, the HAEMS...
Recently, various strategies for energy management have been proposed to improve energy efficiency in smart grids. One key aspect of this is the use of microgrids. To effectively manage energy in a residential microgrid, advanced computational tools...
Increased focus on sustainability and energy decentralization has positively impacted the adoption of nanogrids. With the tremendous growth, load forecasting has become crucial for their daily operation. Since the loads of nanogrids have large variat...
Accurate simulation and prediction of occupants’ energy use behavior are crucial in building energy consumption research. However, few studies have focused on household energy use behavior in severely cold regions that have unique energy use pa...
Load disaggregation determines appliance-level energy consumption unintrusively from aggregated consumption measured by a single meter. Deep neural networks have been proven to have great potential in load disaggregation. In this article, a temporal...
End-User Development has been proposed over the years to allow end users to control and manage their Internet of Things-based environments, such as smart homes. With End-User Development, end users are able to create trigger-action rules or routines...
Nowadays climate change problems have been more and more concerns and urgent in the real world. Especially, the energy power consumption monitoring is a considerate trend having positive effects in decreasing affecting climate change. Non-Intrusive L...
This paper presents an energy-aware Artificial Intelligence of Things framework designed for intelligent remote device control in residential settings. The system architecture is grounded in the Power Administration Device (PAD), a cost-effective and...
Power systems are going through a transition period. Consumers want more active participation in electric system management, namely assuming the role of producers–consumers, prosumers in short. The prosumers’ energy production is heavily...
In smart grid, energy management is an indispensable for reducing energy cost of consumers while maximizing user comfort and alleviating the peak to average ratio and carbon emission under real time pricing approach. In contrast, the emergence of bid...
Accurate short-term load forecasting is vital for smart-city energy management, enabling real-time grid stability and sustainable demand response. This study introduces a cloud-enabled hybrid forecasting framework that integrates Seasonal Autoregress...
Mini-grids powered by photovoltaic generators or other renewable energy sources have the potential to bring electricity to the 17% of the world’s population, mainly in rural areas, that are currently un-served. However, designing and managing a mini-...
The global energy crisis, driven by economic and political disruptions, has intensified efforts to transition toward a more competitive and sustainable society. This study, framed within the context of SDG 7, examines the influence of knowledge, psyc...
Energy efficiency policies are made to reduce the energy necessary to achieve a given level of indoor heating and to decrease the greenhouse gas emission worldwide. National and state regulators routinely tighten the energy efficiency building code a...
The paper is concerned with predicting energy consumption in the production and product usage stages and searching for possible changes in product design to reduce energy consumption. The prediction of energy consumption uses parametric models based...
Grid-connected photovoltaic (PV) capacity is increasing and is currently estimated to account for 3.0% of worldwide energy generation. One strategy to balance fluctuating PV power is to incentivize self-consumption by shifting certain loads. The pote...
Monitoring electricity consumption in residential buildings is an important way to help reduce energy usage. Nonintrusive load monitoring is a technique to separate the total electrical load of a single household into specific appliance loads. This p...
With technological advancements, domestic appliances are leveraging smart technologies for getting smarter through learning from their past usage to enhance user comfort and energy efficiency. Among these, ceiling fans, though widely used in Lower- a...
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