A simulation and control model for building energy management

Fanti, Maria Pia; Mangini, Agostino Marcello; Roccotelli, Michele

doi:10.1016/j.conengprac.2017.11.010

Cited by 44 publications

(19 citation statements)

References 21 publications

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Section: Related Workmentioning

confidence: 99%

“…The algorithm enables a single agent with an appropriate algorithm to take a series of best actions and solve complex tasks. Another study in [ 42 ] proposed a control system to optimize the energy consumption in community buildings. The control system analyzes the energy consumption and cost of appliances using a simulator during the peak and off-peak time horizon and makes aware the user to minimize energy consumption.…”

Section: Related Workmentioning

confidence: 99%

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A Multi-Objective Approach for Optimal Energy Management in Smart Home Using the Reinforcement Learning

Diyan

Silva

Han

2020

Sensors

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Maintaining a fair use of energy consumption in smart homes with many household appliances requires sophisticated algorithms working together in real time. Similarly, choosing a proper schedule for appliances operation can be used to reduce inappropriate energy consumption. However, scheduling appliances always depend on the behavior of a smart home user. Thus, modeling human interaction with appliances is needed to design an efficient scheduling algorithm with real-time support. In this regard, we propose a scheduling algorithm based on human appliances interaction in smart homes using reinforcement learning (RL). The proposed scheduling algorithm divides the entire day into various states. In each state, the agents attached to household appliances perform various actions to obtain the highest reward. To adjust the discomfort which arises due to performing inappropriate action, the household appliances are categorized into three groups i.e., (1) adoptable, (2) un-adoptable, (3) manageable. Finally, the proposed system is tested for the energy consumption and discomfort level of the home user against our previous scheduling algorithm based on least slack time phenomenon. The proposed scheme outperforms the Least Slack Time (LST) based scheduling in context of energy consumption and discomfort level of the home user.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A Multi-Objective Approach for Optimal Energy Management in Smart Home Using the Reinforcement Learning

Diyan

Silva

Han

2020

Sensors

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“…According to the items mentioned above, many engineers have tried to develop different predictive and evaluative tools the primary aim in order to produce an optimal approximation of building energy consumption. So far, building energy has been modelled in a number of attempts [9,10]. Generally, there are two common methods for EPB evaluation: Forward modelling and inverse modelling [11].…”

Section: Introductionmentioning

confidence: 99%

Predicting Heating and Cooling Loads in Energy-Efficient Buildings Using Two Hybrid Intelligent Models

et al. 2019

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Today, energy conservation is more and more stressed as great amounts of energy are being consumed for varying applications. This study aimed to evaluate the application of two robust evolutionary algorithms, namely genetic algorithm (GA) and imperialist competition algorithm (ICA) for optimizing the weights and biases of the artificial neural network (ANN) in the estimation of heating load (HL) and cooling load (CL) of the energy-efficient residential buildings. To this end, a proper dataset was provided composed of relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area, glazing area distribution, as the HL and CL influential factors. The optimal structure of each model was achieved through a trial and error process and to evaluate the accuracy of the designed networks, we used three well-known accuracy criterions. As the result of applying GA and ICA, the performance error of ANN decreased respectively by 17.92% and 23.22% for the HL, and 21.13% and 24.53% for CL in the training phase, and 20.84% and 23.74% for HL, and 27.57% and 29.10% for CL in the testing phase. The mentioned results demonstrate the superiority of the ICA-ANN model compared to GA-ANN and ANN.

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“…Pia Fanti et al 15 are interested in managing the energy consumption of electrical appliances in buildings using renewable energies (PV, eolienne. .…”

Section: Introductionmentioning

confidence: 99%

Performance-related dependability evaluation of multi-source renewable energy systems using deterministic and stochastic Petri nets

Hellel

Hamaci

Ziani

2018

Energy & Environment

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On a global scale, the energy demand continues to increase. The costs of fossil fuels are uncertain. The liberalization of the electricity market and an environmental conscience are the levers for the development of renewable energies. They have reached a technical maturity that allows them to become an important segment of the energy industry. Their insertion in the energy mix poses new challenges compared to traditional energy sources. With abundant potential still under-exploited, wind and solar (photovoltaic) energy are economically and environmentally beneficial. However, their intermittent nature decreases their energy efficiency when used individually. The use of multi-source systems to combine these renewable energy sources is considered a reliable solution. In this context, we are interested in determining the performances of multi-source renewable energy systems, related to their dependability. Our approach is based on the use of deterministic and stochastic Petri nets for analyzing the dynamic behavior of these systems. By using the developed model for analyzing functional and dysfunctional behavior, we determine the instants of passage from functioning state to breakdown state of multi-source renewable energy systems for calculating their reliability. After that, the overall production time, for these systems, is defined by developing mathematical models to calculate duration of failure and waiting time of each component, according to the architecture of the system to be studied.

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A simulation and control model for building energy management

Cited by 44 publications

References 21 publications

A Multi-Objective Approach for Optimal Energy Management in Smart Home Using the Reinforcement Learning

A Multi-Objective Approach for Optimal Energy Management in Smart Home Using the Reinforcement Learning

Predicting Heating and Cooling Loads in Energy-Efficient Buildings Using Two Hybrid Intelligent Models

Performance-related dependability evaluation of multi-source renewable energy systems using deterministic and stochastic Petri nets

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