Dynamic Prediction of Building HVAC Energy Consumption by Ensemble Learning Approach

Tian, Weisong; Lei, Chengwei; Tian, Mucun

doi:10.1109/csci46756.2018.00055

Cited by 6 publications

(2 citation statements)

References 17 publications

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“…Ref. [24] focused on the use of ensemble learning techniques to predict the power consumption of a building with given weather forecast information. They noted that the gradient boosted trees yielded the best performance among the different ensemble methods used.…”

Section: Related Workmentioning

confidence: 99%

Performance Analysis of Machine Learning Algorithms for Energy Demand–Supply Prediction in Smart Grids

2022

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The use of machine learning (ML) algorithms for power demand and supply prediction is becoming increasingly popular in smart grid systems. Due to the fact that there exist many simple ML algorithms/models in the literature, the question arises as to whether there is any significant advantage(s) among these different ML algorithms, particularly as it pertains to power demand/supply prediction use cases. Toward answering this question, we examined six well-known ML algorithms for power prediction in smart grid systems, including the artificial neural network, Gaussian regression (GR), k-nearest neighbor, linear regression, random forest, and support vector machine (SVM). First, fairness was ensured by undertaking a thorough hyperparameter tuning exercise of the models under consideration. As a second step, power demand and supply statistics from the Eskom database were selected for day-ahead forecasting purposes. These datasets were based on system hourly demand as well as renewable generation sources. Hence, when their hyperparameters were properly tuned, the results obtained within the boundaries of the datasets utilized showed that there was little/no significant difference in the quantitative and qualitative performance of the different ML algorithms. As compared to photovoltaic (PV) power generation, we observed that these algorithms performed poorly in predicting wind power output. This could be related to the unpredictable wind-generated power obtained within the time range of the datasets employed. Furthermore, while the SVM algorithm achieved the slightly quickest empirical processing time, statistical tests revealed that there was no significant difference in the timing performance of the various algorithms, except for the GR algorithm. As a result, our preliminary findings suggest that using a variety of existing ML algorithms for power demand/supply prediction may not always yield statistically significant comparative prediction results, particularly for sources with regular patterns, such as solar PV or daily consumption rates, provided that the hyperparameters of such algorithms are properly fine tuned.

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Section: Related Workmentioning

confidence: 99%

Performance Analysis of Machine Learning Algorithms for Energy Demand–Supply Prediction in Smart Grids

2022

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“…In order to achieve low power consumption and low cost, and to ensure the safe and stable operation of the air conditioning system, reliable algorithms and selection of hardware and software are required to ensure the function of the system. [3][4]. However, at present, most of the air-conditioning intelligent control systems of engineering projects are idle and resources are wasted.…”

Section: Introductionmentioning

confidence: 99%

Research on Intelligent Control System of Air Conditioning based on Internet of Things Intelligent Control System of Air Conditioning

Zhang¹

2022

IJACSA

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The current air conditioning intelligent control system cannot achieve the ideal energy-saving effect. The indoor temperature and humidity control is not good enough either. Therefore, an intelligent air-conditioning control system based on Internet of Things technology is designed. The hardware part of the system includes system control motherboard, sensor module, execution control structure, wireless communication module and access layer. The software includes the design of communication layer, the design of monitoring management, and the design of intelligent indoor air-conditioning temperature remote control algorithm. The experimental results show that the control effect of the intelligent air conditioner is more accurate and energy-saving, the opening degree of the air conditioning valve is larger, and the comfort is improved. The indoor temperature and humidity of the proposed system are both more ideal.

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Predicting the renewable energy consumption in 2026 by using a recursive moving average model

Khatibi

Jahangir

Astaraei

et al. 2022

International Journal of Ambient Energy

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Dynamic Prediction of Building HVAC Energy Consumption by Ensemble Learning Approach

Cited by 6 publications

References 17 publications

Performance Analysis of Machine Learning Algorithms for Energy Demand–Supply Prediction in Smart Grids

Performance Analysis of Machine Learning Algorithms for Energy Demand–Supply Prediction in Smart Grids

Research on Intelligent Control System of Air Conditioning based on Internet of Things Intelligent Control System of Air Conditioning

Predicting the renewable energy consumption in 2026 by using a recursive moving average model

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