Modeling and forecasting building energy consumption: A review of data-driven techniques

Sustainable Cities and Society, Journal Year: 2019, Volume and Issue: 48, P. 101533 - 101533

Published: April 14, 2019

Language: Английский

---

Forecasting: theory and practice

International Journal of Forecasting, Journal Year: 2022, Volume and Issue: 38(3), P. 705 - 871

Published: Jan. 20, 2022

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds future is both exciting challenging, with individuals organisations seeking to minimise risks maximise utilities. large number forecasting applications calls for a diverse set methods tackle real-life challenges. This article provides non-systematic review theory practice forecasting. We provide an overview wide range theoretical, state-of-the-art models, methods, principles, approaches prepare, produce, organise, evaluate forecasts. then demonstrate how such theoretical concepts are applied in variety contexts. do not claim this exhaustive list applications. However, we wish our encyclopedic presentation will offer point reference rich work undertaken over last decades, some key insights practice. Given its nature, intended mode reading non-linear. cross-references allow readers navigate through various topics. complement covered by lists free or open-source software implementations publicly-available databases.

Language: Английский

---

Random Forest based hourly building energy prediction

Energy and Buildings, Journal Year: 2018, Volume and Issue: 171, P. 11 - 25

Published: April 18, 2018

Language: Английский

---

A review of machine learning in building load prediction

Applied Energy, Journal Year: 2021, Volume and Issue: 285, P. 116452 - 116452

Published: Jan. 13, 2021

Language: Английский

---

Predictive modelling for solar thermal energy systems: A comparison of support vector regression, random forest, extra trees and regression trees

Journal of Cleaner Production, Journal Year: 2018, Volume and Issue: 203, P. 810 - 821

Published: Aug. 28, 2018

Predictive analytics play an important role in the management of decentralised energy systems. Prediction models uncontrolled variables (e.g., renewable sources generation, building consumption) are required to optimally manage electrical and thermal grids, making informed decisions for fault detection diagnosis. The paper presents a comprehensive study compare tree-based ensemble machine learning (random forest – RF extra trees ET), decision (DT) support vector regression (SVR) predict useful hourly from solar collector system. developed were compared based on their generalisation ability (stability), accuracy computational cost. It was found that ET have comparable predictive power equally applicable predicting (USTE), with root mean square error (RMSE) values 6.86 7.12 testing dataset, respectively. Amongst studied algorithms, DT is most computationally efficient method as it requires significantly less training time. However, accurate (RMSE = 8.76) than ET. time SVR 1287.80 ms, which approximately three times higher

Language: Английский

---

A Novel CNN-GRU-Based Hybrid Approach for Short-Term Residential Load Forecasting

IEEE Access, Journal Year: 2020, Volume and Issue: 8, P. 143759 - 143768

Published: Jan. 1, 2020

Electric energy forecasting domain attracts researchers due to its key role in saving resources, where mainstream existing models are based on Gradient Boosting Regression (GBR), Artificial Neural Networks (ANNs), Extreme Learning Machine (ELM) and Support Vector (SVM). These encounter high-level of non-linearity between input data output predictions limited adoptability real-world scenarios. Meanwhile, demands more robustness, higher prediction accuracy generalization ability for implementation. In this paper, we achieve the mentioned tasks by developing a hybrid sequential learning-based model that employs Convolution Network (CNN) Gated Recurrent Units (GRU) into unified framework accurate consumption prediction. The proposed has two major phases: (1) refinement (2) training, phase applies preprocessing strategies over raw data. training phase, CNN features extracted from dataset fed GRU, is selected as optimal observed have enhanced sequence learning abilities after extensive experiments. an effective alternative previous terms computational complexity well accuracy, representative features' extraction potentials CNNs effectual gated structure multi-layered GRU. experimental evaluation datasets reveal better performance our method preciseness efficiency. achieved smallest error rate Appliances Energy Prediction (AEP) Individual Household Power Consumption (IHEPC) datasets, when compared other baseline models.

Language: Английский

---

Gradient boosting machine for modeling the energy consumption of commercial buildings

Energy and Buildings, Journal Year: 2017, Volume and Issue: 158, P. 1533 - 1543

Published: Nov. 26, 2017

Language: Английский

---

A review of the-state-of-the-art in data-driven approaches for building energy prediction

Energy and Buildings, Journal Year: 2020, Volume and Issue: 221, P. 110022 - 110022

Published: April 30, 2020

Language: Английский

---

A review on renewable energy and electricity requirement forecasting models for smart grid and buildings

Sustainable Cities and Society, Journal Year: 2020, Volume and Issue: 55, P. 102052 - 102052

Published: Jan. 18, 2020

Language: Английский

---

Building energy consumption prediction for residential buildings using deep learning and other machine learning techniques

Journal of Building Engineering, Journal Year: 2021, Volume and Issue: 45, P. 103406 - 103406

Published: Oct. 12, 2021

Language: Английский

---