An Innovative Approach for Forecasting of Energy Requirements to Improve a Smart Home Management System Based on BLE

Collotta, Mario; Pau, Giovanni

doi:10.1109/tgcn.2017.2671407

Cited by 92 publications

(36 citation statements)

References 43 publications

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“…A total of 45.5% of the scientific articles summarized in Table S10, presented in the Supplementary Materials file, analyzed smart buildings in general; the same percentage of papers considered smart homes, while the remaining 9% analyzed both smart homes and smart buildings. The authors of these scientific papers make use of different types of sensors in their analyses, including sensors for registering the electricity consumption [22]; Wireless Sensor Networks (WSNs) [23,45,96]; Passive Infrared (PIR) sensors or motion detectors [75,97]; smart metering systems and sensors installed by the residential consumer, corresponding to 15 individual appliances [95]; weather sensors [12]; flowmeter sensors [43]; temperature sensors, external humidity sensors, solar radiation sensors [98]; thermal sensors [2]; and door/window entry point sensors, electricity power usage sensors, bed/sofa pressure sensors, and flood sensors [75].…”

Section: Regressionmentioning

confidence: 99%

“…With respect to the reasons for implementing the Neural Networks for regression purposes integrated with sensor devices in smart buildings, these were mainly related to forecasting electricity consumption [12,22,23,45,95]; identifying the occurrence of a specific pattern in a Water Management System (WMS) [43]; indoor temperature monitoring and forecasting [96,98]; human behavior recognition [2,75]; and short-term prediction of occupancy [97].…”

Section: Regressionmentioning

confidence: 99%

“…With respect to the devised research methods, in [22], The performance metrics considered in the scientific papers using the Neural Networks for Regression Purposes integrated with sensor devices in smart buildings included Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) [12,22,23,45]; Coefficient of Determination (R 2 ) [23]; Mean Absolute Percentage Error (MAPE) [23,45]; Mean Squared Error (MSE) [45,95,98]; Correlation Coefficient (R) [95,98]; the differences between the real consumption and the forecasted ones [95]; Pearson Coefficient [12]; Accuracy [43,97]; Precision, Recall, and F-Measure [43]; Average Error and Error Rate [2]; Mean Absolute Error (MAE) [96]; and Root Mean Squared Error (RMSE) [75].…”

Section: Regressionmentioning

confidence: 99%

See 2 more Smart Citations

A Review of the Recent Developments in Integrating Machine Learning Models with Sensor Devices in the Smart Buildings Sector with a View to Attaining Enhanced Sensing, Energy Efficiency, and Optimal Building Management

et al. 2019

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Lately, many scientists have focused their research on subjects like smart buildings, sensor devices, virtual sensing, buildings management, Internet of Things (IoT), artificial intelligence in the smart buildings sector, improving life quality within smart homes, assessing the occupancy status information, detecting human behavior with a view to assisted living, maintaining environmental health, and preserving natural resources. The main purpose of our review consists of surveying the current state of the art regarding the recent developments in integrating supervised and unsupervised machine learning models with sensor devices in the smart building sector with a view to attaining enhanced sensing, energy efficiency and optimal building management. We have devised the research methodology with a view to identifying, filtering, categorizing, and analyzing the most important and relevant scientific articles regarding the targeted topic. To this end, we have used reliable sources of scientific information, namely the Elsevier Scopus and the Clarivate Analytics Web of Science international databases, in order to assess the interest regarding the above-mentioned topic within the scientific literature. After processing the obtained papers, we finally obtained, on the basis of our devised methodology, a reliable, eloquent and representative pool of 146 papers scientific works that would be useful for developing our survey. Our approach provides a useful up-to-date overview for researchers from different fields, which can be helpful when submitting project proposals or when studying complex topics such those reviewed in this paper. Meanwhile, the current study offers scientists the possibility of identifying future research directions that have not yet been addressed in the scientific literature or improving the existing approaches based on the body of knowledge. Moreover, the conducted review creates the premises for identifying in the scientific literature the main purposes for integrating Machine Learning techniques with sensing devices in smart environments, as well as purposes that have not been investigated yet.

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Section: Regressionmentioning

confidence: 99%

Section: Regressionmentioning

confidence: 99%

Section: Regressionmentioning

confidence: 99%

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A Review of the Recent Developments in Integrating Machine Learning Models with Sensor Devices in the Smart Buildings Sector with a View to Attaining Enhanced Sensing, Energy Efficiency, and Optimal Building Management

et al. 2019

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“…This is one of the reasons why research efforts have been focused on innovative efficient climate regulation, to guarantee optimal conditions for the crop, at the same time minimizing energy consumption as the literature has done in smart-home [36,37].…”

Section: A Preliminary Greenhouse Analysismentioning

confidence: 99%

An Innovative Adaptive Control System to Regulate Microclimatic Conditions in a Greenhouse

2017

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Abstract:In the recent past home automation has been expanding its objectives towards new solutions both inside the smart home and in its outdoor spaces, where several new technologies are available. This work has developed an approach to integrate intelligent microclimatic greenhouse control into integrated home automation. Microclimatic control of greenhouses is a critical issue in agricultural practices, due to often common sudden daily variation of climatic conditions, and to its potentially detrimental effect on plant growth. A greenhouse is a complex thermodynamic system where indoor temperature and relative humidity have to be closely monitored to facilitate plant growth and production. This work shows an adaptive control system tailored to regulate microclimatic conditions in a greenhouse, by using an innovative combination of soft computing applications. In particular, a neural network solution has been proposed in order to forecast the climatic behavior of greenhouse, while a parallel fuzzy scheme approach is carried out in order to adjust the air speed of fan-coil and its temperature. The proposed combined approach provides a better control of greenhouse climatic conditions due to the system's capability to base instantaneous solutions both on real measured variables and on forecasted climatic change. Several simulation campaigns were carried out to perform accurate neural network and fuzzy schemes, aimed at obtaining respectively a minimum forecasted error value and a more appropriate fuzzification and de-fuzzification process. A Matlab/Simulink solution implemented with a combined approach and its relevant obtained performance is also shown in present study, demonstrating that through controlled parameters it will be possible to maintain a better level of indoor climatic conditions. In the present work we prove how with a forecast of outside temperature at the next time-instant and rule-based controller monitoring of cooling or heating air temperatures and air velocities of devices that regulate the indoor micro-climate inside, a better adjustment of the conditions of comfort for crops is achievable.

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“…SHEM deals with the real-time monitoring and arranging of various home appliances, based on user's preferences via intelligent ambient systems controlled by a human-machine interface in smart houses, with the aim of electricity cost reduction and energy utilization efficiency improvements [7].…”

Section: Introductionmentioning

confidence: 99%

Smart Home Energy Management

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An Innovative Approach for Forecasting of Energy Requirements to Improve a Smart Home Management System Based on BLE

Cited by 92 publications

References 43 publications

A Review of the Recent Developments in Integrating Machine Learning Models with Sensor Devices in the Smart Buildings Sector with a View to Attaining Enhanced Sensing, Energy Efficiency, and Optimal Building Management

A Review of the Recent Developments in Integrating Machine Learning Models with Sensor Devices in the Smart Buildings Sector with a View to Attaining Enhanced Sensing, Energy Efficiency, and Optimal Building Management

An Innovative Adaptive Control System to Regulate Microclimatic Conditions in a Greenhouse

Smart Home Energy Management

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