A practical artificial intelligence-based approach for predictive control in commercial and institutional buildings

Cotrufo, Nunzio; Saloux, Étienne; Hardy, Jean-Marc; Candanedo, José A.; Platon, Radu

doi:10.1016/j.enbuild.2019.109563

Cited by 50 publications

(26 citation statements)

References 29 publications

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“…AI solutions rely on using Machine Learning (ML) algorithms by making a training process using real (data-driven) or simulated (model-driven) data from a set of sensors, softsensors or simulation environments. Cotrufo et al [14] tested five ML algorithms to effectively manage electric heaters and avoid the usage of a gas boiler. To do so, they created a model predictive control by training five ML algorithms and compared the obtained results against the set-back strategy (called BAU), to study how ML algorithms are capable of reducing the operation of the gas boiler.…”

Section: A Ai and Expert Rule Systemsmentioning

confidence: 99%

Designing a generalised reward for Building Energy Management Reinforcement Learning agents

Mulero

Arregi

Beitia

et al. 2021

2021 6th International Conference on Smart and Sustainable Technologies (SpliTech)

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The reduction of the carbon footprint of buildings is a challenging task, partly due to the conflicting goals of maximising occupant comfort and minimising energy consumption. An intelligent management of Heating, Ventilation and Air Conditioning (HVAC) systems is creating a promising research line in which the creation of suitable algorithms could reduce energy consumption maintaining occupants' comfort. In this regard, Reinforcement Learning (RL) approaches are giving a good balance between data requirements and intelligent operations to control building systems. However, there is a gap concerning how to create a generalised reward signal that can train RL agents without delimiting the problem to a specific or controlled scenario. To tackle it, an analysis and discussion is presented about the necessary requirements for the creation of generalist rewards, with the objective of laying the foundations that allow the creation of generalist intelligent agents for building energy management.

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Section: A Ai and Expert Rule Systemsmentioning

confidence: 99%

Designing a generalised reward for Building Energy Management Reinforcement Learning agents

Mulero

Arregi

Beitia

et al. 2021

2021 6th International Conference on Smart and Sustainable Technologies (SpliTech)

View full text Add to dashboard Cite

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“…They have offered a promising pathway for the development of prediction models [19]. Scholars have reported their application in the energy prediction of buildings due to their ability to overcome the limits encountered by existing models [20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

“…ANN was the most suitable energy-saving strategy, while the others were more adapted to thermal comfort strategies. Cotrufo et al [23] proposed a methodology for the development of an AI-based model for the thermal control of institutional buildings. The results showed that the Gaussian process regression (GPR) outperformed the ANN, support vector machine (SVM), decision tree (DT), and random forest (RF) models.…”

Section: Introductionmentioning

confidence: 99%

Use of Machine Learning Methods for Indoor Temperature Forecasting

et al. 2021

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Improving the energy efficiency of the building sector has become an increasing concern in the world, given the alarming reports of greenhouse gas emissions. The management of building energy systems is considered an essential means for achieving this goal. Predicting indoor temperature constitutes a critical task for the management strategies of these systems. Several approaches have been developed for predicting indoor temperature. Determining the most effective has thus become a necessity. This paper contributes to this objective by comparing the ability of seven machine learning algorithms (ML) and the thermal gray box model to predict the indoor temperature of a closed room. The comparison was conducted on a set of data recorded in a room of the Laboratory of Civil Engineering and geo-Environment (LGCgE) at Lille University. The results showed that the best prediction was obtained with the artificial neural network (ANN) and extra trees regressor (ET) methods, which outperformed the thermal gray box model.

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“…Noteworthy changes to decrease building operating expenses and assets arrangement may be reached through cutting-edge control techniques. Some of these techniques utilize forecasts of unsettling influences to foresee future building behavior and select an ideal arrangement of activities [4].…”

Section: Introductionmentioning

confidence: 99%

“…Subtopics selection: the filtered articles were analyzed so that a relationship could be made between them, and then subtopics selected that would be used in the paper. 4.…”

mentioning

confidence: 99%

A Review of Energy Consumption Forecasting in Smart Buildings: Methods, Input Variables, Forecasting Horizon and Metrics

et al. 2020

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Buildings are among the largest energy consumers in the world. As new technologies have been developed, great advances have been made in buildings, turning conventional buildings into smart buildings. These smart buildings have allowed for greater supervision and control of the energy resources within the buildings, taking steps to energy management strategies to achieve significant energy savings. The forecast of energy consumption in buildings has been a very important element in these energy strategies since it allows adjusting the operation of buildings so that energy can be used more efficiently. This paper presents a review of energy consumption forecasting in smart buildings for improving energy efficiency. Different forecasting methods are studied in nonresidential and residential buildings. Following this, the literature is analyzed in terms of forecasting objectives, input variables, forecasting methods and prediction horizon. In conclusion, the paper examines future challenges for building energy consumption forecasting.

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A practical artificial intelligence-based approach for predictive control in commercial and institutional buildings

Cited by 50 publications

References 29 publications

Designing a generalised reward for Building Energy Management Reinforcement Learning agents

Designing a generalised reward for Building Energy Management Reinforcement Learning agents

Use of Machine Learning Methods for Indoor Temperature Forecasting

A Review of Energy Consumption Forecasting in Smart Buildings: Methods, Input Variables, Forecasting Horizon and Metrics

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