A novel improved model for building energy consumption prediction based on model integration

Applied Energy, Journal Year: 2020, Volume and Issue: 262, P. 114561 - 114561

Published: Feb. 8, 2020

Language: Английский

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Prediction of home energy consumption based on gradient boosting regression tree

Energy Reports, Journal Year: 2021, Volume and Issue: 7, P. 1246 - 1255

Published: Feb. 21, 2021

Energy consumption prediction of buildings has drawn attention in the related literature since it is very complex and affected by various factors. Hence, a challenging work accurately estimating energy improving its efficiency. Therefore, effective management forecasting are now becoming important advocating conservation. Many researchers on saving increasing utilization rate energy. Prior works about combine software hardware to provide reasonable suggestions for users based analyzed results. In this paper, an innovative model established simulate predict electrical buildings. proposed model, data more predicted using gradient boosting regression tree algorithm. By comparing performance index Root Mean Square Error different models through experiments shown that obtains lower values testing data. More detailed comparison with other existing show superior prediction.

Language: Английский

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Mapping the spatial and temporal variability of flood hazard affected by climate and land-use changes in the future

Journal of Environmental Management, Journal Year: 2021, Volume and Issue: 298, P. 113551 - 113551

Published: Aug. 17, 2021

The predicts current and future flood risk in the Kalvan watershed of northwestern Markazi Province, Iran. To do this, 512 non-flood locations were identified mapped. Twenty flood-risk factors selected to model using several machine learning techniques: conditional inference random forest (CIRF), gradient boosting (GBM), extreme (XGB) their ensembles. investigate (year 2050) effects changing climates land use on risk, a general circulation (GCM) with representative concentration pathways (RCPs) 2.6 8.5 scenarios by 2050 was tested for impacts 8 precipitation variables. In addition, uses prepared CA-Markov model. performances models validated Receiver Operating Characteristic-Area Under Curve (ROC-AUC) other statistical analyses. AUC value ROC curve indicates that ensemble had highest predictive power (AUC = 0.83) followed GBM 0.80), XGB 0.79), CIRF 0.78). results climate changes flood-prone areas showed classified as having moderate very high will increase 2050. Due occurring climates, area increased predictions from all four models. areal proportion classes zones under RCP scenario have changed following proportions distribution Very Low −12.04 %, −8.56 Moderate +1.56 High +11.55 +7.49 %. has caused present percentages: −14.48 −6.35 +4.54 +10.61 +5.67 mapping can aid planners hazard managers efforts mitigate impacts.

Language: Английский

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A review of ensemble learning and data augmentation models for class imbalanced problems: Combination, implementation and evaluation

Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 244, P. 122778 - 122778

Published: Dec. 10, 2023

Class imbalance (CI) in classification problems arises when the number of observations belonging to one class is lower than other. Ensemble learning combines multiple models obtain a robust model and has been prominently used with data augmentation methods address problems. In last decade, strategies have added enhance ensemble methods, along new such as generative adversarial networks (GANs). A combination these applied many studies, evaluation different combinations would enable better understanding guidance for application domains. this paper, we present computational study evaluate prominent benchmark CI We general framework that evaluates 9 Our objective identify most effective improving performance on imbalanced datasets. The results indicate can significantly improve find traditional synthetic minority oversampling technique (SMOTE) random (ROS) are not only selected problems, but also computationally less expensive GANs. vital development novel handling

Language: Английский

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Modelling heating and cooling energy demand for building stock using a hybrid approach

Energy and Buildings, Journal Year: 2021, Volume and Issue: 235, P. 110740 - 110740

Published: Jan. 14, 2021

Language: Английский

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A comprehensive review of machine learning‐based methods in landslide susceptibility mapping

Geological Journal, Journal Year: 2023, Volume and Issue: 58(6), P. 2283 - 2301

Published: Jan. 3, 2023

Landslide susceptibility mapping (LSM) has been widely used as an important reference for development and construction planning to mitigate the potential social‐eco impact caused by landslides. Originally, most of those maps were generated judgements experts, which is time‐consuming laborious, whose accuracy difficult be quantified because subjective effects. With machine learning algorithms methods data collection, big artificial intelligence have now popularized in this field, significantly improving efficiency. Various learning‐based methods, mainly including conventional learning, deep transfer applied compared LSM different areas previous researchers. Nevertheless, none them can effective all cases. Although proven more accurate than data‐rich situations, latter sometimes popularly LSM, there not that much field train a network perfectly. In rigorous situation when very limited data, approaches are several researchers, contributed improve workability data‐limited areas. Such technical explosion promoted application landslide maps, thus contributing mitigating associated with This paper comprehensively reviews whole process generating based on introduces compares commonly discusses topics future research.

Language: Английский

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An analysis of 45 large-scale wastewater sites in England to estimate SARS-CoV-2 community prevalence

Nature Communications, Journal Year: 2022, Volume and Issue: 13(1)

Published: July 25, 2022

Accurate surveillance of the COVID-19 pandemic can be weakened by under-reporting cases, particularly due to asymptomatic or pre-symptomatic infections, resulting in bias. Quantification SARS-CoV-2 RNA wastewater used infer infection prevalence, but uncertainty sensitivity and considerable variability has meant that accurate measurement remains elusive. Here, we use data from 45 sewage sites England, covering 31% population, estimate prevalence within 1.1% estimates representative surveys (with 95% confidence). Using machine learning phenomenological models, show differences between sampled sites, flow rate, influence estimation require careful interpretation. We find signals appear 4-5 days earlier comparison clinical testing are coincident with suggesting a leading indicator for symptomatic viral infections. Surveillance viruses complements strengthens surveillance, significant implications public health.

Language: Английский

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Distributed Anomaly Detection in Smart Grids: A Federated Learning-Based Approach

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 7157 - 7179

Published: Jan. 1, 2023

The smart grid integrates Information and Communication Technologies (ICT) into the traditional power to manage generation, distribution, consumption of electrical energy. Despite its many advantages, it faces significant challenges, such as detecting abnormal behaviours in grid. Identifying anomalous helps discover unusual user consumption, faulty infrastructure, outages, equipment failures, energy thefts, or cyberattacks. Machine learning (ML)-based techniques on meter data has shown remarkable results anomaly detection. However, ML-based detection requires meters share local with a central server, which raises concerns regarding security privacy. Server-based model training additional requirement centralised computing power, reliable network communication, large bandwidth capacity, latency issues, all affect real-time performance. Motivated by these concerns, we propose Federated Learning (FL)-based scheme where ML models are trained locally without sharing thus ensuring In proposed approach, global is downloaded from server for on-device training. After training, parameters sent improve model. We secure parameter updates adversaries using SSL/TLS protocol. Using standard datasets, investigate performance federated observe that FL achieve comparable while Further, our study shows FL-based perform efficiently terms memory, CPU usage, at edge devices suitable implementation resource-constrained environments, meters,

Language: Английский

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A sophisticated model for rating water quality

The Science of The Total Environment, Journal Year: 2023, Volume and Issue: 868, P. 161614 - 161614

Published: Jan. 18, 2023

Here, we present the Irish Water Quality Index (IEWQI) model for assessing transitional and coastal water quality in an effort to improve method develop a tool that can be used by environmental regulators abate pollution Ireland. The developed has been associated with adoption of standards formulated waterbodies according framework directive legislation regulator water. consists five identical components, including (i) indicator selection technique is select crucial indicator; (ii) sub-index (SI) function rescaling various indicators' information into uniform scale; (iii) weight estimating values based on relative significance real-time quality; aggregation computing index (WQI) score; (v) score interpretation scheme state quality. IEWQI was Cork Harbour, applied four Ireland, using 2021 data summer winter seasons order evaluate sensitivity terms spatio-temporal resolution waterbodies. efficiency uncertainty were also analysed this research. In different magnitudes domains, shows higher application domains during winter. addition, results reveal architecture may effective reducing avoid eclipsing ambiguity problems. findings study could efficient reliable assessment more accurately any geospatial domain.

Language: Английский

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Data-driven prediction and optimization toward net-zero and positive-energy buildings: A systematic review

Building and Environment, Journal Year: 2023, Volume and Issue: 242, P. 110578 - 110578

Published: July 4, 2023

Language: Английский

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