Reducing Energy Consumption in the Workplace via IoT-Allowed Behavioural Change Interventions

Ramallo-González, Alfonso P.; Bardaki, Cleopatra; Kotsopoulos, Dimosthenis; Tomat, Valentina; González-Vidal, Aurora; Ruiz, Pedro J. Fernandez; Skarmeta-Gómez, Antonio F.

doi:10.3390/buildings12060708

Cited by 19 publications

(9 citation statements)

References 35 publications

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“…We show that these savings range from 2 to 17%. While our framework differs from other research projects with similar objective in terms of setup and parameters (source of readings, input, type of analysis, and form of recommendation, dataset) making direct comparisons difficult, we should note that our findings are in accordance with other recent studies, such as Ramallo-González et al ( 2022 ) that measured how much energy can be saved if users follow prompts or recommendations for energy preservation, or Sardianos et al ( 2020 ) that reported savings from the application of their recommender system on a office building setup.…”

Section: Experimental Evaluationsupporting

confidence: 87%

“…A similar framework is proposed in the BENEFFICE project (Garbi et al, 2019 ) where the authors propose to leverage IoT devices to capture appliance usage patterns and recommendations, incentives and challenges to gradually shape a better energy consumer profile. Most recently, the ENTROPY project (Ramallo-González et al, 2022 ) presented a series of educational interventions that promote energy saving in a timed and personalized manner. Going one step beyond, the EM3 project (Alsalemi et al, 2019 ) builds on the habitual behavioral change and combines context-aware recommender systems and IoT in order to maximize the probability of recommendation acceptance and thus the impact of the behavioral change policy.…”

Section: Related Workmentioning

confidence: 99%

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Real-time recommendations for energy-efficient appliance usage in households

et al. 2022

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According to several studies, the most influencing factor in a household's energy consumption is user behavior. Changing user behavior to improve energy usage leads to efficient energy consumption, saving money for the consumer and being more friendly for the environment. In this work we propose a framework that aims at assisting households in improving their energy usage by providing real-time recommendations for efficient appliance use. The framework allows for the creation of household-specific and appliance-specific energy consumption profiles by analyzing appliance usage patterns. Based on the household profile and the actual electricity use, real-time recommendations notify users on the appliances that can be switched off in order to reduce consumption. For instance, if a consumer forgets their A/C on at a time that it is usually off (e.g., when there is no one at home), the system will detect this as an outlier and notify the consumer. In the ideal scenario, a household has a smart meter monitoring system installed, that records energy consumption at the appliance level. This is also reflected in the datasets available for evaluating such systems. However, in the general case, the household may only have one main meter reading. In this case, non-intrusive load monitoring (NILM) techniques, which monitor a house's energy consumption using only one meter, and data mining algorithms that disaggregate the consumption into appliance level, can be employed. In this paper, we propose an end-to-end solution to this problem, starting with the energy disaggregation process, and the creation of user profiles that are then fed to the pattern mining and recommendation process, that through an intuitive UI allows users to further refine their energy consumption preferences and set goals. We employ the UK-DALE (UK Domestic Appliance-Level Electricity) dataset for our experimental evaluations and the proof-of-concept implementation. The results show that the proposed framework accurately captures the energy consumption profiles of each household and thus the generated recommendations are matching the actual household energy habits and can help reduce their energy consumption by 2–17%.

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Section: Experimental Evaluationsupporting

confidence: 87%

Section: Related Workmentioning

confidence: 99%

Real-time recommendations for energy-efficient appliance usage in households

et al. 2022

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“… Selecting a subset of the features that are most valuable to develop energy forecasts and evaluate their quality 13 , 14 . Sending personalised recommendations to HVAC users use with the goal of reducing energy consumption 15 . Forecasting indoor temperature per room 16 .…”

Section: Background and Summarymentioning

confidence: 99%

“…Sending personalised recommendations to HVAC users use with the goal of reducing energy consumption 15 .…”

Section: Background and Summarymentioning

confidence: 99%

PLEIAData: consumption, HVAC, temperature, weather and motion sensor data for smart buildings applications

2023

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The current cost that energy represents is crucial in a field like climate control which has high energy demands, therefore its reduction must be prioritized. The expansion of ICT and IoT come with an extensive deployment of sensors and computation infrastructure creating an opportunity to analyze and optimize energy management. Data on building internal and external conditions is essential for developing efficient control strategies in order to minimize energy consumption while maintaining users’ comfort inside. We here present a dataset that provides key features that could be useful for a wide range of applications in the context of modeling temperature and consumption via Artificial Intelligence algorithms. The data gathering has taken place for almost 1 year in the Pleiades building of the University of Murcia, which is a pilot building of the European project PHOENIX aiming to improve building energy efficiency.

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“…In a smart city, the existing waste management system is transformed into a smart system via the IoT concept. Several studies have attempted to develop an intelligent system for real-time, low-cost waste collection [ 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 ]. The following literature review focuses on current research using IoT concepts to implement intelligent solid waste management systems in Mecca.…”

Section: Introductionmentioning

confidence: 99%

IoT-Based Waste Management System in Formal and Informal Public Areas in Mecca

Abdullah

Al-wesabi

Mohammed

et al. 2022

IJERPH

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Urban areas worldwide are in the race to become smarter, and the Kingdom of Saudi Arabia (KSA) is no exception. Many of these have envisaged a chance to establish devoted municipal access networks to assist all kinds of city administration and preserve services needing data connectivity. Organizations unanimously concentrate on sustainability issues with key features of general trends, particularly the combination of the 3Rs (reduce waste, reuse and recycle resources). This paper demonstrates how the incorporation of the Internet of Things (IoT) with data access networks, geographic information systems and combinatorial optimization can contribute to enhancing cities’ administration systems. A waste-gathering approach based on supplying smart bins is introduced by using an IoT prototype embedded with sensors, which can read and convey bin volume data over the Internet. However, from another perspective, the population and residents’ attitudes directly affect the control of the waste management system. The conventional waste collection system does not cover all areas in the city. It works based on a planned scheme that is implemented by the authorized organization focused on specific popular and formal areas. The conventional system cannot observe a real-time update of the bin status to recognize whether the waste level condition is ‘full,’ ‘not full,’ or ‘empty.’ This paper uses IoT in the container and trucks that secure the overflow and separation of waste. Waste source locations and population density influence the volume of waste generation, especially waste food, as it has the highest amount of waste generation. The open public area and the small space location problems are solved by proposing different truck sizes based on the waste type. Each container is used for one type of waste, such as food, plastic and others, and uses the optimization algorithm to calculate and find the optimal route toward the full waste container. In this work, the situations in KSA are evaluated, and relevant aspects are explored. Issues relating to the sustainability of organic waste management are conceptually analyzed. A genetic-based optimization algorithm for waste collection transportation enhances the performance of waste-gathering truck management. The selected routes based on the volume status and free spaces of the smart bins are the most effective through those obtainable towards the urgent smart bin targets. The proposed system outperforms other systems by reducing the number of locations and smart bins that have to be visited by 46% for all waste types, whereas the conventional and existing systems have to visit all locations every day, resulting in high cost and consumption time.

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Reducing Energy Consumption in the Workplace via IoT-Allowed Behavioural Change Interventions

Cited by 19 publications

References 35 publications

Real-time recommendations for energy-efficient appliance usage in households

Real-time recommendations for energy-efficient appliance usage in households

PLEIAData: consumption, HVAC, temperature, weather and motion sensor data for smart buildings applications

IoT-Based Waste Management System in Formal and Informal Public Areas in Mecca

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