A Non-Linear Autoregressive Model for Indoor Air-Temperature Predictions in Smart Buildings

Aliberti, Alessandro; Bottaccioli, Lorenzo; Macii, Enrico; Cataldo, Santa Di; Acquaviva, Andrea; Patti, Edoardo

doi:10.3390/electronics8090979

Cited by 28 publications

(16 citation statements)

References 43 publications

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“…Figure 14 presents the system diagram of the IoT architecture. Aliberti et al [56] assessed an IoT-based solution for indoor air temperature forecasting. Forecast-based technologies, for instance, demand response and demand side management, are used to reduce energy waste.…”

Section: Predictive Temperature Controlmentioning

confidence: 99%

Recent Advances in Internet of Things (IoT) Infrastructures for Building Energy Systems: A Review

Yaïci

Krishnamurthy²,

Entchev

et al. 2021

Sensors

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This paper summarises a literature review on the applications of Internet of Things (IoT) with the aim of enhancing building energy use and reducing greenhouse gas emissions (GHGs). A detailed assessment of contemporary practical reviews and works was conducted to understand how different IoT systems and technologies are being developed to increase energy efficiencies in both residential and commercial buildings. Most of the reviewed works were invariably related to the dilemma of efficient heating systems in buildings. Several features of the central components of IoT, namely, the hardware and software needed for building controls, are analysed. Common design factors across the many IoT systems comprise the selection of sensors and actuators and their powering techniques, control strategies for collecting information and activating appliances, monitoring of actual data to forecast prospect energy consumption and communication methods amongst IoT components. Some building energy applications using IoT are provided. It was found that each application presented has the potential for significant energy reduction and user comfort improvement. This is confirmed in two case studies summarised, which report the energy savings resulting from implementing IoT systems. Results revealed that a few elements are user-specific then need all be considered in the decision processes. Last, based on the studies reviewed, a few aspects of prospective research were recommended.

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Section: Predictive Temperature Controlmentioning

confidence: 99%

Recent Advances in Internet of Things (IoT) Infrastructures for Building Energy Systems: A Review

Yaïci

Krishnamurthy²,

Entchev

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Furthermore, we implemented three more popular methods using other platforms: support vector regression (SVR) using the LibSVM library was implemented in C++, 3 and Generalized Regression Neural Network (GRNN) and Extreme Learning Machine (ELM) with Gaussian kernels were both implemented in MATLAB. 4 Moreover, we trained the regressors by exploiting the values reported in Table 3, and stated in the R package documentation to tune the algorithm hyper-parameters.…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

“…Within smart buildings, the automation of existing residential as well as commercial buildings (built prior to modern low-or zero-energy buildings) plays a significant role, as such buildings make up the majority of energy consumption. The EU has pointed to the development of efficient building energy management systems as key to achieving the identified objectives due to the fact that buildings account for 40% of energy consumption and 36% of total CO 2 emissions within the EU [3,72]. The majority of energy in those buildings is consumed by Heating, Ventilation, and Air Conditioning (HVAC) systems, which have strong impact on households comfort as well as on the environment [29].…”

Section: Introductionmentioning

confidence: 99%

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A comparison of machine learning algorithms for forecasting indoor temperature in smart buildings

et al. 2020

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The international community has largely recognized that the Earth's climate is changing. Mitigating its global effects requires international actions. The European Union (EU) is leading several initiatives focused on reducing the problems. Specifically, the Climate Action tries to both decrease EU greenhouse gas emissions and improve energy efficiency by reducing the amount of primary energy consumed, and it has pointed to the development of efficient building energy management systems as key. In traditional buildings, households are responsible for continuously monitoring and controlling the installed Heating, Ventilation, and Air Conditioning (HVAC) system. Unnecessary energy consumption might occur due to, for example, forgetting devices turned on, which overwhelms users due to the need to tune the devices manually. Nowadays, smart buildings are automating this process by automatically tuning HVAC systems according to user preferences in order to improve user satisfaction and optimize energy consumption. Towards achieving this goal, in this paper, we compare 36 Machine Learning algorithms that could be used to forecast indoor temperature in a smart building. More specifically, we run experiments using real data to compare their accuracy in terms of R-coefficient and Root Mean Squared Error and their performance in terms of Friedman rank. The results reveal that the ExtraTrees regressor has obtained the highest average accuracy (0.97%) and performance (0,058%) over all horizons.

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“…In order to effectively analyze the quality of power signals, research [30] proposes a method of signal feature capture and fault identification based on the ANN combined with discrete wavelet transform and Parseval's theorem. ANN for air-temperature predictions in smart buildings was developed in [31] in order to obtain better energy control. Short term forecasting prediction of the photovoltaic plant power output by using ANNs can be found in [32,33].…”

Section: Introductionmentioning

confidence: 99%

Genetic Algorithm Approach to Design of Multi-Layer Perceptron for Combined Cycle Power Plant Electrical Power Output Estimation

et al. 2019

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In this paper a genetic algorithm (GA) approach to design of multi-layer perceptron (MLP) for combined cycle power plant power output estimation is presented. Dataset used in this research is a part of publicly available UCI Machine Learning Repository and it consists of 9568 data points (power plant operating regimes) that is divided on training dataset that consists of 7500 data points and testing dataset containing 2068 data points. Presented research was performed with aim of increasing regression performances of MLP in comparison to ones available in the literature by utilizing heuristic algorithm. The GA described in this paper is performed by using mutation and crossover procedures. These procedures are utilized for design of 20 different chromosomes in 50 different generations. MLP configurations that are designed with GA implementation are validated by using Bland - Altman (B-A) analysis. By utilizing GA, MLP with five hidden layers of 80,25,65,75 and 80 nodes, respectively, is designed. For aforementioned MLP, k - fold cross-validation is performed in order to examine its generalization performances. The Root Mean Square Error ( R M S E ) value achieved with aforementioned MLP is 4.305 , that is significantly lower in comparison with MLP presented in available literature, but still higher than several complex algorithms such as KStar and tree based algorithms.

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A Non-Linear Autoregressive Model for Indoor Air-Temperature Predictions in Smart Buildings

Cited by 28 publications

References 43 publications

Recent Advances in Internet of Things (IoT) Infrastructures for Building Energy Systems: A Review

Recent Advances in Internet of Things (IoT) Infrastructures for Building Energy Systems: A Review

A comparison of machine learning algorithms for forecasting indoor temperature in smart buildings

Genetic Algorithm Approach to Design of Multi-Layer Perceptron for Combined Cycle Power Plant Electrical Power Output Estimation

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