Artificial intelligence for energy conservation in buildings

Dounis, Anastasios I.

doi:10.3763/aber.2009.0408

Cited by 80 publications

(39 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Development of a multi-agent system requires first dividing a problem into sub problems that can be solved by the individual, representative agents. The solutions to these sub problems can then be combined to change the current global state through agent-to-agent coordination [10].…”

Section: Controlling Building Systemsmentioning

confidence: 99%

Improving energy efficiency of cyber physical systems using multi-agent based control

Simões

Bhattarai

2012

2012 IEEE Industry Applications Society Annual Meeting

View full text Add to dashboard Cite

This paper presents an intelligent control strategy for improving energy efficiency of a commercial building using a multi agent system architecture. The proposed MAS based control minimizes the amount of required energy while maintaining comfort for the occupants of a building. Models for thermal and electrical systems for a prescribed area of a building are developed and the generality of the solution to encompass any building with any internal structure. These models are controlled by an intelligent MAS system. The energy consumption using the proposed MAS approach will be compared to traditional controllers and energy consumption results will be compared with other benchmark indices.

show abstract

Section: Controlling Building Systemsmentioning

confidence: 99%

Improving energy efficiency of cyber physical systems using multi-agent based control

Simões

Bhattarai

2012

2012 IEEE Industry Applications Society Annual Meeting

View full text Add to dashboard Cite

show abstract

“…Some of these methods are used to predict consumption by correlating it with influencing variables, such as climate conditions or energy prices. Interested readers are referred to [1]- [4] for a more comprehensive discussion about building modeling with focus on electrical demand forecasting. Moreover, to account for the evolution of future building energy management systems, there are also some representative approaches which combine some of the above modeling methods to optimize predictive performance, such as semi-parametric regression models used to forecast the contribution of load from some non-linear variable [5], exponential smoothing [6], multivariate state-space models and seasonal time series models [7]- [9].…”

Section: Introductionmentioning

confidence: 99%

Demand forecasting at low aggregation levels using Factored Conditional Restricted Boltzmann Machine

Mocanu

Nguyen

Gibescu

et al. 2016

2016 Power Systems Computation Conference (PSCC)

View full text Add to dashboard Cite

Abstract-The electrical demand forecasting problem can be regarded as a nonlinear time series prediction problem depending on many complex factors since it is required at various aggregation levels and at high temporal resolution. To solve this challenging problem, various time series and machine learning approaches have been proposed in the literature. As an evolution of neural network-based prediction methods, deep learning techniques are expected to increase the prediction accuracy by allowing stochastic formulations and bi-directional connections between neurons. In this paper, we investigate a newly developed deep learning model for time series prediction, namely Factored Conditional Restricted Boltzmann Machine (FCRBM), and extend it for electrical demand forecasting. The assessment is made on the EcoGrid dataset, originating from the Bornholm island experiment in Denmark, consisting of aggregated electric power consumption, local price and meteorological data collected from 1900 customers. The households are equipped with local generation and smart appliances capable of responding to realtime pricing signals. The results show that for the short-term (5 minute to 1 day ahead) prediction problems solved here, FCRBM outperforms the benchmark machine learning approach, i.e. Support Vector Machine.

show abstract

“…These methods are used to predict building energy consumption by correlating energy consumption with influencing variables such as weather and energy cost. Interested readers are referred to [1] and [2] for a more comprehensive discussion of the application of building energy systems, and more recently reviews [3] and [4]. Moreover, to shape the evolution of future buildings systems there are also some hybrid approaches which combine some of the above models to optimize predictive performance, such as [5]- [8].…”

Section: Introductionmentioning

confidence: 99%

Comparison of machine learning methods for estimating energy consumption in buildings

Mocanu

Nguyen

Gibescu

et al. 2014

2014 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)

View full text Add to dashboard Cite

Abstract-The increasing number of decentralized renewable energy sources together with the grow in overall electricity consumption introduce many new challenges related to dimensioning of grid assets and supply-demand balancing. Approximately 40% of the total energy consumption is used to cover the needs of commercial and office buildings. To improve the design of the energy infrastructure and the efficient deployment of resources, new paradigms have to be thought up. Such new paradigms need automated methods to dynamically predict the energy consumption in buildings. At the same time these methods should be easily expandable to higher levels of aggregation such as neighbourhoods and the power distribution grid. Predicting energy consumption for a building is complex due to many influencing factors, such as weather conditions, performance and settings of heating and cooling systems, and the number of people present. In this paper, we investigate a newly developed stochastic model for time series prediction of energy consumption, namely the Conditional Restricted Boltzmann Machine (CRBM), and evaluate its performance in the context of building automation systems. The assessment is made on a real dataset consisting of 7 weeks of hourly resolution electricity consumption collected from a Dutch office building. The results showed that for the energy prediction problem solved here, CRBMs outperform Artificial Neural Networks (ANNs), and Hidden Markov Models (HMMs).

show abstract

Artificial intelligence for energy conservation in buildings

Cited by 80 publications

References 38 publications

Improving energy efficiency of cyber physical systems using multi-agent based control

Improving energy efficiency of cyber physical systems using multi-agent based control

Demand forecasting at low aggregation levels using Factored Conditional Restricted Boltzmann Machine

Comparison of machine learning methods for estimating energy consumption in buildings

Contact Info

Product

Resources

About