PSO-Stacking improved ensemble model for campus building energy consumption forecasting based on priority feature selection

Cao, Yisheng; Liu, Gang; Sun, Jian; Bavirisetti, Durga Prasad; Xiao, Gang

doi:10.1016/j.jobe.2023.106589

Cited by 13 publications

(6 citation statements)

References 46 publications

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“…This process is essential for enhancing model performance by reducing the dimensionality of the data and eliminating irrelevant or redundant features. Therefore, PSO has been used as an effective technique in many fields, including feature selection [18]. The grey wolf optimizer (GWO) is one of the most recent and popular swarm intelligence algorithms.…”

Section: Imentioning

confidence: 99%

An Optimized Hybrid Approach for Feature Selection Based on Chi-Square and Particle Swarm Optimization Algorithms

Abdo,

Mostafa,

Abdel-Hamid

2024

Data

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Feature selection is a significant issue in the machine learning process. Most datasets include features that are not needed for the problem being studied. These irrelevant features reduce both the efficiency and accuracy of the algorithm. It is possible to think about feature selection as an optimization problem. Swarm intelligence algorithms are promising techniques for solving this problem. This research paper presents a hybrid approach for tackling the problem of feature selection. A filter method (chi-square) and two wrapper swarm intelligence algorithms (grey wolf optimization (GWO) and particle swarm optimization (PSO)) are used in two different techniques to improve feature selection accuracy and system execution time. The performance of the two phases of the proposed approach is assessed using two distinct datasets. The results show that PSOGWO yields a maximum accuracy boost of 95.3%, while chi2-PSOGWO yields a maximum accuracy improvement of 95.961% for feature selection. The experimental results show that the proposed approach performs better than the compared approaches.

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Section: Imentioning

confidence: 99%

An Optimized Hybrid Approach for Feature Selection Based on Chi-Square and Particle Swarm Optimization Algorithms

Abdo,

Mostafa,

Abdel-Hamid

2024

Data

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“…HEMSs are often considered as multi-objective optimization arrangements, since they have several features intended to solve multiple problems in order to improve energy efficiency including cost minimization, user comfort, etc. Several algorithms can be used to solve these problems, such as the commonly used genetic algorithms developed by Yang et al or the particle swarm optimization algorithms proposed by Cao et al (Shanghai University of Electric Power) [22,23]. Other less explored alternatives in the current literature include the cuckoo search algorithm and strawberry algorithm studied by Aslam et al and the ant colony algorithm analyzed by Güven et al [24,25].…”

Section: Literature Reviewmentioning

confidence: 99%

Assessment of Current Smart House Solutions: The Case of Portugal

Rego,

Castro,

Silva

2023

Energies

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The world is facing many challenges to keep up with growing energy demand and greenhouse gas and pollutant emissions. A possible solution is to integrate renewable energy sources (RESs) into residences and create energy management systems (EMSs) that reduce consumption and relieve the main grid in critical hours. This paper is focused on analyzing different EMSs using solar photovoltaic (PV) modules and three energy storage systems (ESSs) to increase savings without affecting the user’s needs, with thirty-three power consumption profiles and three energy tariffs over a 25-year investment to compare which solution presents the best economic result in each of the 297 analyzed scenarios. The results show that the higher the overall daily consumption values, the greater the profits for all EMSs. The lowest demand profiles lead to the best self-consumption and self-sufficiency results. PV-only systems with a tri-hourly tariff have the best overall results. In the case of battery operation, each case should be carefully analyzed in order to choose the correct technologies and tariff. The results also allowed us to verify that even though having more PV modules generates more energy, it can make a project less economically desirable.

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“…These particles store their best position as well as the global position. It is this combination of local and global information that gives rise to 'swarm intelligence' [14]. In our study we implemented XGBoost and linear regression algorithms to select the best features.…”

Section: Particle Swarm Optimization (Pso) Algorithmmentioning

confidence: 99%

Predicting Road Traffic Collisions Using a Two-Layer Ensemble Machine Learning Algorithm.

Oyoo,

Wekesa,

Ogada

2023

Preprint

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Road collisions are among the world’s critical issues causing many casualties, deaths, and economic losses. To deal with this scenario, precise analysis is required. In this paper, we use Machine Learning (ML) techniques to assess and predict road traffic collisions. An ensemble machine learning model with two layers was developed to analyze simulation data from the driving simulator. The first (base) layer integrates supervised learning techniques namely, k- Nearest Neighbors (k-NN) AdaBoost, Naive Bayes (NB), and Decision Trees (DT). The second layer predicts road collisions by combining the base layer outputs by employing the stacking ensemble method using logistic regression as a meta-classifier. The data for this study were imbalanced; therefore, we performed a SMOTE resampling strategy to handle the dataset imbalance. To simplify the model, the PSO algorithm was used to select the most important features in our dataset. The two-layer ensemble model proposed had the best outcomes with an accuracy of 88%, F1 score of 83%, and an AUC of 86% as compared with k-NN, DT, NB, and AdaBoost.

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PSO-Stacking improved ensemble model for campus building energy consumption forecasting based on priority feature selection

Cited by 13 publications

References 46 publications

An Optimized Hybrid Approach for Feature Selection Based on Chi-Square and Particle Swarm Optimization Algorithms

An Optimized Hybrid Approach for Feature Selection Based on Chi-Square and Particle Swarm Optimization Algorithms

Assessment of Current Smart House Solutions: The Case of Portugal

Predicting Road Traffic Collisions Using a Two-Layer Ensemble Machine Learning Algorithm.

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