Short-term prediction of building energy consumption employing an improved extreme gradient boosting model: A case study of an intake tower

Lu, Hongfang; Cheng, Fei-Fei; Ma, Xin; Hu, Gang

doi:10.1016/j.energy.2020.117756

Cited by 89 publications

(34 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ensemble models, such as RF, DT, gradient boosting have emerged as a promising approach for modeling building energy data. Lu et al developed a tree-based ML model-extreme gradient boosting (so-called XGBoost) to predict daily electrical load from the City of Bloomington Intake Tower in Indiana, USA 24 . This tower plays an important role in residents’ lives since its function in collecting water from reservoirs and transporting it to hydroelectric power plants or water treatment plants.…”

Section: Previous Workmentioning

confidence: 99%

Proposing a hybrid metaheuristic optimization algorithm and machine learning model for energy use forecast in non-residential buildings

Ngo

Truong

et al. 2022

Sci Rep

View full text Add to dashboard Cite

The building sector is the largest energy consumer accounting for 40% of global energy usage. An energy forecast model supports decision-makers to manage electric utility management. Identifying optimal values of hyperparameters of prediction models is challenging. Therefore, this study develops a novel time-series Wolf-Inspired Optimized Support Vector Regression (WIO-SVR) model to predict 48-step-ahead energy consumption in buildings. The proposed model integrates the support vector regression (SVR) and the grey wolf optimizer (GWO) in which the SVR model serves as a prediction engine while the GWO is used to optimize the hyperparameters of the SVR model. The 30-min energy data from various buildings in Vietnam were adopted to validate model performance. Buildings include one commercial building, one hospital building, three authority buildings, three university buildings, and four office buildings. The dataset is divided into the learning data and the test data. The performance of the WIO-SVR was superior to baseline models including the SVR, random forests (RF), M5P, and decision tree learner (REPTree). The WIO-SVR model obtained the highest value of correlation coefficient (R) with 0.90. The average root-mean-square error (RMSE) of the WIO-SVR was 2.02 kWh which was more accurate than those of the SVR model with 10.95 kWh, the RF model with 16.27 kWh, the M5P model with 17.73 kWh, and the REPTree model with 26.44 kWh. The proposed model improved 442.0–1207.9% of the predictive accuracy in RMSE. The reliable WIO-SVR model provides building managers with useful references in efficient energy management.

show abstract

Section: Previous Workmentioning

confidence: 99%

Proposing a hybrid metaheuristic optimization algorithm and machine learning model for energy use forecast in non-residential buildings

Ngo

Truong

et al. 2022

Sci Rep

View full text Add to dashboard Cite

show abstract

“…For the selection of the models that were used to test the proposed strategy, the most popular data-driven models for forecasting demand [20][21][22][23][24][25] in buildings were considered. In addition to this, models that have not been as widely used as temporal convolutional network and temporal fusion transformer were included; the reason for this was to see if these models that have been promising in other areas could bring better results.…”

Section: Selected Forecasting Modelsmentioning

confidence: 99%

A Data-Driven Forecasting Strategy to Predict Continuous Hourly Energy Demand in Smart Buildings

et al. 2021

View full text Add to dashboard Cite

Smart buildings seek to have a balance between energy consumption and occupant comfort. To make this possible, smart buildings need to be able to foresee sudden changes in the building’s energy consumption. With the help of forecasting models, building energy management systems, which are a fundamental part of smart buildings, know when sudden changes in the energy consumption pattern could occur. Currently, different forecasting methods use models that allow building energy management systems to forecast energy consumption. Due to this, it is increasingly necessary to have appropriate forecasting models to be able to maintain a balance between energy consumption and occupant comfort. The objective of this paper is to present an energy consumption forecasting strategy that allows hourly day-ahead predictions. The presented forecasting strategy is tested using real data from two buildings located in Valladolid, Spain. Different machine learning and deep learning models were used to analyze which could perform better with the proposed strategy. After establishing the performance of the models, a model was assembled using the mean of the prediction values of the top five models to obtain a model with better performance.

show abstract

“…Some recent boosting variants such as extreme gradient boosting (XGBoost) [18], light gradient boosting machine (LGBM) [19], and categorical boosting (CatBoost) [20] have been developed by focusing on the increment of speed and predictive performance, and achieved robustness results in real applications and forecasting competitions. In recent years, boosting-tree-based algorithms have been widely applied in many areas, namely, computer vision [21], biology [22], chemistry [23], energy [24], etc. In particular, boosting has provided great advances in the energy sector in terms of highest predictive performance [25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Short-Term Energy Forecasting Using Machine-Learning-Based Ensemble Voting Regression

2022

View full text Add to dashboard Cite

Meeting the required amount of energy between supply and demand is indispensable for energy manufacturers. Accordingly, electric industries have paid attention to short-term energy forecasting to assist their management system. This paper firstly compares multiple machine learning (ML) regressors during the training process. Five best ML algorithms, such as extra trees regressor (ETR), random forest regressor (RFR), light gradient boosting machine (LGBM), gradient boosting regressor (GBR), and K neighbors regressor (KNN) are trained to build our proposed voting regressor (VR) model. Final predictions are performed using the proposed ensemble VR and compared with five selected ML benchmark models. Statistical autoregressive moving average (ARIMA) is also compared with the proposed model to reveal results. For the experiments, usage energy and weather data are gathered from four regions of Jeju Island. Error measurements, including mean absolute percentage error (MAPE), mean absolute error (MAE), and mean squared error (MSE) are computed to evaluate the forecasting performance. Our proposed model outperforms six baseline models in terms of the result comparison, giving a minimum MAPE of 0.845% on the whole test set. This improved performance shows that our approach is promising for symmetrical forecasting using time series energy data in the power system sector.

show abstract

Short-term prediction of building energy consumption employing an improved extreme gradient boosting model: A case study of an intake tower

Cited by 89 publications

References 37 publications

Proposing a hybrid metaheuristic optimization algorithm and machine learning model for energy use forecast in non-residential buildings

Proposing a hybrid metaheuristic optimization algorithm and machine learning model for energy use forecast in non-residential buildings

A Data-Driven Forecasting Strategy to Predict Continuous Hourly Energy Demand in Smart Buildings

Short-Term Energy Forecasting Using Machine-Learning-Based Ensemble Voting Regression

Contact Info

Product

Resources

About