Prediction of home energy consumption based on gradient boosting regression tree

In theory, building automation and management systems (BAMSs) can provide all the components and functionalities required for analyzing and operating buildings. However, in reality, these systems can only ensure the control of heating ventilation and air conditioning system systems. Therefore, many other tasks are left to the operator, e.g. evaluating buildings’ performance, detecting abnormal energy consumption, identifying the changes needed to improve efficiency, ensuring the security and privacy of end-users, etc. To that end, there has been a movement for developing artificial intelligence (AI) big data analytic tools as they offer various new and tailor-made solutions that are incredibly appropriate for practical buildings’ management. Typically, they can help the operator in (i) analyzing the tons of connected equipment data; and; (ii) making intelligent, efficient, and on-time decisions to improve the buildings’ performance. This paper presents a comprehensive systematic survey on using AI-big data analytics in BAMSs. It covers various AI-based tasks, e.g. load forecasting, water management, indoor environmental quality monitoring, occupancy detection, etc. The first part of this paper adopts a well-designed taxonomy to overview existing frameworks. A comprehensive review is conducted about different aspects, including the learning process, building environment, computing platforms, and application scenario. Moving on, a critical discussion is performed to identify current challenges. The second part aims at providing the reader with insights into the real-world application of AI-big data analytics. Thus, three case studies that demonstrate the use of AI-big data analytics in BAMSs are presented, focusing on energy anomaly detection in residential and office buildings and energy and performance optimization in sports facilities. Lastly, future directions and valuable recommendations are identified to improve the performance and reliability of BAMSs in intelligent buildings.

show abstract

“… 2020 ), gradient boosting regression tree (GBRT) (Nie et al. 2021 ), LightGBM (Park et al. 2021 ; Wang et al.…”

Section: Overview Of Ai-big Data Analytic Frameworkmentioning

confidence: 99%

AI-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The gradient boosting model has a higher forecasting accuracy than the RF model, according to the findings Gu et al [ 57 ] This study introduced a hybrid strategy focused on GBDT, correlation analysis, and EWT for forecasting the London Metal Exchange Nickel settlement price. The developed model was evaluated on three sets of financial data, and the findings demonstrate that when compared to the GBDT, it optimized the TIC output by 16.33% Nie et al [ 58 ] A new energy consumption prediction model is developed to simulate and predict building electrical energy use. The gradient boosting regression tree approach is used in the proposed model to more precisely estimate energy consumption data.…”

Section: Literature Reviewmentioning

confidence: 99%

A Comparative Study of Demand Forecasting Models for a Multi-Channel Retail Company: A Novel Hybrid Machine Learning Approach

et al. 2022

View full text Add to dashboard Cite

Demand forecasting has been a major concern of operational strategy to manage the inventory and optimize the customer satisfaction level. The researchers have proposed many conventional and advanced forecasting techniques, but no one leads to complete accuracy. Forecasting is equally important in manufacturing as well as retail companies. In this study, the performances of five regression techniques of machine learning, viz. random forest (RF), extreme gradient boosting (XGBoost), gradient boosting, adaptive boosting (AdaBoost), and artificial neural network (ANN) algorithms, are compared with a proposed hybrid (RF-XGBoost-LR) model for sales forecasting of a retail chain considering the various parameters of forecasting accuracy. The weekly sales data of a US-based retail company is considered in the analysis of the forecasts undertaking the attributes affecting the sale such as the temperature of the region and the size of the store. It is observed that the hybrid RF-XGBoost-LR outperformed the other models measured against various metrics of performance. This study may help the industry decision-maker to understand and improve the methods of forecasting.

show abstract

“…GBRT is a method based on ensemble learning, which trains multiple weak classifiers and determines the final classification results by voting [41]. This sequential model construction process is in the form of function gradient descent; that is, a new tree is added in each step to minimize the loss function [42,43]. In order to simplify the selection of experimental parameters, we set the parameter η to 1.…”

Section: Numerical Analysismentioning

confidence: 99%

Multigranulation-Based Granularity Selection for Intuitionistic Fuzzy Weighted Neighborhood IoT Data

Tang

Zhang

et al. 2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

Granular computing is to represent, construct, and process information granules formalized in many different approaches, and different formal approaches emphasize the same fundamental facet in different ways. In this paper, we present the multigranulation rough set models in the intuitionistic fuzzy neighborhood information system generated by Internet of Things (IoT) data with weighted features and develop the basic properties of the proposed models. Moreover, the multigranulation-based optimal granularity selection approach is established by introducing the concept of granularity significance. The experimental results on eight IoT datasets demonstrate that the proposed methods exhibit better efficiency by comparing three classical methods under the intuitionistic fuzzy weighted neighborhood information system.

show abstract

Prediction of home energy consumption based on gradient boosting regression tree

Cited by 104 publications

References 36 publications

AI-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives

AI-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives

A Comparative Study of Demand Forecasting Models for a Multi-Channel Retail Company: A Novel Hybrid Machine Learning Approach

Multigranulation-Based Granularity Selection for Intuitionistic Fuzzy Weighted Neighborhood IoT Data

Contact Info

Product

Resources

About