Optimal Building Energy Management: Part II—Control

Bénard, C.; Guerrier, B.; Rosset-Louërat, Marie-Minerve

doi:10.1115/1.2929976

Cited by 35 publications

(22 citation statements)

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“…Many solutions are developed for energy saving and optimal utilization in the literature [8][9][10]. One of the major issues with conventional solutions for energy optimization includes the problem of overshooting.…”

Section: Related Workmentioning

confidence: 99%

An Improved Optimization Function for Maximizing User Comfort with Minimum Energy Consumption in Smart Homes

Ullah

Kim

2017

Energies

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Abstract:In the smart home environment, efficient energy management is a challenging task. Solutions are needed to achieve a high occupant comfort level with minimum energy consumption. User comfort is measured in terms of three fundamental parameters: (a) thermal comfort, (b) visual comfort and (c) air quality. Temperature, illumination and CO 2 sensors are used to collect indoor contextual information. In this paper, we have proposed an improved optimization function to achieve maximum user comfort in the building environment with minimum energy consumption. A comprehensive formulation is done for energy optimization with detailed analysis. The Kalman filter algorithm is used to remove noise in sensor readings by predicting actual parameter values. For optimization, we have used genetic algorithm (GA) and particle swarm optimization (PSO) algorithms and performed comparative analysis with a baseline scheme on real data collected for a one-month duration in our lab's indoor environment. Experimental results show that the proposed optimization function has achieved a 27.32% and a 31.42% reduction in energy consumption with PSO and GA, respectively. The user comfort index was also improved by 10% i.e., from 0.86 to 0.96. GA-based optimization results were better than PSO, as it has achieved almost the same user comfort with 4.19% reduced energy consumption. Results show that the proposed optimization function gives better results than the baseline scheme in terms of user comfort and the amount of consumed energy. The proposed system can help with collecting the data about user preferences and energy consumption for long-term analysis and better decision making in the future for efficient resource utilization and overall profit maximization.

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Section: Related Workmentioning

confidence: 99%

An Improved Optimization Function for Maximizing User Comfort with Minimum Energy Consumption in Smart Homes

Ullah

Kim

2017

Energies

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“…Development of building energy management systems have been investigated based on various control systems [10][11][12]. Among the major reasons for energy inefficiencies and overutilization is that trivial systems often do not consider environmental factors and user occupancy.…”

Section: Related Workmentioning

confidence: 99%

A Prediction Mechanism of Energy Consumption in Residential Buildings Using Hidden Markov Model

2018

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Internet of Things (IoT) is considered as one of the future disruptive technologies, which has the potential to bring positive change in human lifestyle and uplift living standards. Many IoT-based applications have been designed in various fields, e.g., security, health, education, manufacturing, transportation, etc. IoT has transformed conventional homes into Smart homes. By attaching small IoT devices to various appliances, we cannot only monitor but also control indoor environment as per user demand. Intelligent IoT devices can also be used for optimal energy utilization by operating the associated equipment only when it is needed. In this paper, we have proposed a Hidden Markov Model based algorithm to predict energy consumption in Korean residential buildings using data collected through smart meters. We have used energy consumption data collected from four multi-storied buildings located in Seoul, South Korea for model validation and results analysis. Proposed model prediction results are compared with three well-known prediction algorithms i.e., Support Vector Machine (SVM), Artificial Neural Network (ANN) and Classification and Regression Trees (CART). Comparative analysis shows that our proposed model achieves 2.96% better than ANN results in terms of root mean square error metric, 6.09% better than SVM and 9.03% better than CART results. To further establish and validate prediction results of our proposed model, we have performed temporal granularity analysis. For this purpose, we have evaluated our proposed model for hourly, daily and weekly data aggregation. Prediction accuracy in terms of root mean square error metric for hourly, daily and weekly data is 2.62, 1.54 and 0.46, respectively. This shows that our model prediction accuracy improves for coarse grain data. Higher prediction accuracy gives us confidence to further explore its application in building control systems for achieving better energy efficiency.

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“…A standard PID controller is a control mechanism [26], widely used in buildings engineering for heating control [5][6][7][8][9]. That is why it was considered as the reference controller and was the basic component of the proposed advanced control schemes (PID-MPC and PID-FLC schemes).…”

Section: Pid Control Schemementioning

confidence: 99%

“…Mathews et al (2008) [6], Levermore et al (1992) [7], Bernard et al (1982) [8], Kolokotsa et al (2005) [9], Ben-Nakhi et al (2001) [10], Kalogirou et al (2000) [11] and Gonzalez et al (2005) [12] presented works related to energy management, cost strategies and energy consumption forecast, while Chen et al (2006) [13], Calvino et al (2004) [14], Kummert et al (2001) [15], Morel et al (2000) [16], Nygard (1990) [17], Lute et al (2000) [18], Liang et al (2005) [19] and Argiriou et al (2001) [20] focus on fuzzy, neural network, optimal or predictive control of thermal conditions in buildings. However, energy management in buildings is not really correlated with energy savings and clear strategies to optimize the use of several (fossil and renewable) energy resources are not defined.…”

Section: Introductionmentioning

confidence: 99%

Heating control schemes for energy management in buildings

Paris

Eynard

Grieu

et al. 2010

Energy and Buildings

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because of both the global energy crisis and the necessary improvement of energy efficiency in buildings, one of the largest sectors of energy consumption and greenhouse gases emissions, a strategy allowing managing energy resources is proposed. Its aim is reducing energy consumption and promoting the use of renewable energy, while ensuring thermal comfort, when heating "multi-energy" buildings, thanks to indoor temperature control schemes. Three schemes (based on a commonly-used PID controller and on the combination of PID and model predictive or fuzzy controllers) were tested in simulation, using dynamic models describing the thermal behavior of a building, and fully met the management strategy's requirements, especially reducing the consumption of fossil energy. Three criteria describing the way energy is used and controlled in real-time were defined with the aim of evaluating the control schemes performance and adapting the strategy to the specific use of a building. The PID-MPC provided the best results while the PID-FLC proved to be a very good compromise, thanks to both the flexibility and the adaptability offered by fuzzy logic, between the easy-todevelop but not-very-efficient PID and the efficient but hard-to-develop PID-MPC.

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Optimal Building Energy Management: Part II—Control

Cited by 35 publications

References 0 publications

An Improved Optimization Function for Maximizing User Comfort with Minimum Energy Consumption in Smart Homes

An Improved Optimization Function for Maximizing User Comfort with Minimum Energy Consumption in Smart Homes

A Prediction Mechanism of Energy Consumption in Residential Buildings Using Hidden Markov Model

Heating control schemes for energy management in buildings

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