Random Forest based hourly building energy prediction

Wang, Zeyu; Wang, Yueren; Zeng, Ruochen; Srinivasan, Ravi S.; Ahrentzen, Sherry

doi:10.1016/j.enbuild.2018.04.008

Cited by 438 publications

(170 citation statements)

References 37 publications

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“…In contrast, small nodesize increases the accuracy of RF classification, but it takes more time. Based on previous studies, this paper set nodesize as 5 [33].…”

Section: Important Parameters Of Modelmentioning

confidence: 99%

Multi-Label Classification Based on Random Forest Algorithm for Non-Intrusive Load Monitoring System

Gao

Jiao

2019

Processes

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Non-intrusive load monitoring (NILM) is an effective method to optimize energy consumption patterns. Since the concept of NILM was proposed, extensive research has focused on energy disaggregation or load identification. The traditional method is to disaggregate mixed signals, and then identify the independent load. This paper proposes a multi-label classification method using Random Forest (RF) as a learning algorithm for non-intrusive load identification. Multi-label classification can be used to determine which categories data belong to. This classification can help to identify the operation states of independent loads from mixed signals without disaggregation. The experiments are conducted in real environment and public data set respectively. Several basic electrical features are selected as the classification feature to build the classification model. These features are also compared to select the most suitable features for classification by feature importance parameters. The classification accuracy and F-score of the proposed method can reach 0.97 and 0.98, respectively.

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“…In contrast, small nodesize increases the accuracy of RF classification, but it takes more time. Based on previous studies, this paper set nodesize as 5 [33].…”

Section: Important Parameters Of Modelmentioning

confidence: 99%

Multi-Label Classification Based on Random Forest Algorithm for Non-Intrusive Load Monitoring System

Gao

Jiao

2019

Processes

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“…The differences between the methods described above and our method are as follows: The forecasting models in previous studies [19][20][21][22] presented excellent prediction performance using a sufficient data set of target buildings. However, our forecasting model can exhibit a satisfactory prediction performance even if the electric load data of the target building is insufficient.…”

Section: Related Workmentioning

confidence: 91%

“…They confirmed that time factors were essential Energies 2020, 13, 886 3 of 37 variables to build the prediction models; moreover, the GBM model exhibited better prediction performance than other models. Wang et al [20] presented an hourly electric energy consumption forecasting model for two institutional buildings based on RF. They considered time factors, weather conditions, and the number of occupants as the input variables of the RF model.…”

Section: Related Workmentioning

confidence: 99%

Solving the Cold-Start Problem in Short-Term Load Forecasting Using Tree-Based Methods

et al. 2020

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An energy-management system requires accurate prediction of the electric load for optimal energy management. However, if the amount of electric load data is insufficient, it is challenging to perform an accurate prediction. To address this issue, we propose a novel electric load forecasting scheme using the electric load data of diverse buildings. We first divide the electric energy consumption data into training and test sets. Then, we construct multivariate random forest (MRF)-based forecasting models according to each building except the target building in the training set and a random forest (RF)-based forecasting model using the limited electric load data of the target building in the test set. In the test set, we compare the electric load of the target building with that of other buildings to select the MRF model that is the most similar to the target building. Then, we predict the electric load of the target building using its input variables via the selected MRF model. We combine the MRF and RF models by considering the different electric load patterns on weekdays and holidays. Experimental results demonstrate that combining the two models can achieve satisfactory prediction performance even if the electric data of only one day are available for the target building.

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“…Machine learning, a subfield of artificial intelligence (AI), in contrast typically applies an algorithmic approach (which may non-linearly transform the data), in order to provide a forecast [6]. Many such algorithms have shown to be effective for forecasting and include decision trees [7], random forest [8,9], gradient boosting machines [10], k-nearest neighbors [11], case-based reasoning [12], support vector machines [13], etc.…”

Section: Of 27mentioning

confidence: 99%

Forecasting Energy Use in Buildings Using Artificial Neural Networks: A Review

2019

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During the past century, energy consumption and associated greenhouse gas emissions have increased drastically due to a wide variety of factors including both technological and population-based. Therefore, increasing our energy efficiency is of great importance in order to achieve overall sustainability. Forecasting the building energy consumption is important for a wide variety of applications including planning, management, optimization, and conservation. Data-driven models for energy forecasting have grown significantly within the past few decades due to their increased performance, robustness and ease of deployment. Amongst the many different types of models, artificial neural networks rank among the most popular data-driven approaches applied to date. This paper offers a review of the studies published since the year 2000 which have applied artificial neural networks for forecasting building energy use and demand, with a particular focus on reviewing the applications, data, forecasting models, and performance metrics used in model evaluations. Based on this review, existing research gaps are identified and presented. Finally, future research directions in the area of artificial neural networks for building energy forecasting are highlighted.

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Random Forest based hourly building energy prediction

Cited by 438 publications

References 37 publications

Multi-Label Classification Based on Random Forest Algorithm for Non-Intrusive Load Monitoring System

Multi-Label Classification Based on Random Forest Algorithm for Non-Intrusive Load Monitoring System

Solving the Cold-Start Problem in Short-Term Load Forecasting Using Tree-Based Methods

Forecasting Energy Use in Buildings Using Artificial Neural Networks: A Review

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