Abstract
In today’s world, smart buildings are considered an overarching system that automates a building’s complex operations and increases security while reducing environmental impact. One of the primary goals of building management systems is to promote sustainable and efficient use of energy, requiring coherent task management and execution of control commands for actuators. This paper proposes a predictive-learning framework based on contextual feature selection and optimal actuator control mechanism for minimizing energy consumption in smart buildings. We aim to assess multiple parameters and select the most relevant contextual features that would optimize energy consumption. We have implemented an artificial neural network-based particle swarm optimization (ANN-PSO) algorithm for predictive learning to train the framework on feature importance. Based on the relevance of attributes, our model was also capable of re-adding features. The extracted features are then applied as input parameters for the training of long short-term memory (LSTM) and optimal control module. We have proposed an objective function using a velocity boost-particle swarm optimization (VB-PSO) algorithm that reduces energy cost for optimal control. We then generated and defined the control tasks based on the fuzzy rule set and optimal values obtained from VB-PSO. We compared our model’s performance with and without feature selection using the root mean square error (RMSE) metric in the evaluation section. This paper also presents how optimal control can reduce energy cost and improve performance resulting from lesser learning cycles and decreased error rates.
Highlights
In recent years, household consumption of energy has drastically increased due to the rapid growth rate observed in the world’s population
Energy consumption prediction is one of the significant prediction problems, and many recent researchers have proposed solutions for energy prediction based on deep learning, such as load forecast-based on pinball loss guided long short-term memory (LSTM) [7], energy use prediction for solar-assisted water heating system [8], and short-term residential load forecasting using LSTM recurrent neural network [9,10]
The authors develop a self-training-based adaptive mechanism to address scaling issues with the increase in smart home appliances [18]. Another scalable and non-intrusive load monitoring approach is presented by Kunjin et al A convolutional neural network (CNN)-based model is proposed for building a multi-branch architecture, with an aim to improve prediction accuracies [19]
Summary
Household consumption of energy has drastically increased due to the rapid growth rate observed in the world’s population. A prediction mechanism must consider the surroundings of the given environment and the fact that the surrounding relevance is variable. Another major concern while implementing a prediction model is selecting the most appropriate features. In case of energy prediction, if the given features in a dataset have no strong relation to the increase or decrease in the energy consumption, there are high chances that the model performance will turn out to be poor. An energy consumption prediction mechanism for smart homes based on a hybrid LSTM network is proposed by Ke et al [6]. We present a predictive learning-based optimal actual control mechanism for smart homes. Results analysis is presented in Section 5; Section 7 concludes the paper with discussions
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