Prediction and optimization of energy consumption in an office building using artificial neural network and a genetic algorithm

Ilbeigi, Marjan; Ghomeishi, Mohammad; Dehghanbanadaki, Ali

doi:10.1016/j.scs.2020.102325

Cited by 194 publications

(63 citation statements)

References 45 publications

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“…It is concluded that the neural network model can be used to evaluate the strength of ultra-high-strength concrete, and the result is better than the least squares method. Literature [12] used BP neural network to simulate the change rate of dynamic elastic modulus of magnesium alloy material after being corroded by the composite salt solution and found that the measured results are consistent with the predicted results and the average error is 2.08%. e neural network model can more accurately predict the relative dynamic elastic modulus change rate of the magnesium alloy after the corrosion of the composite salt solution.…”

Section: Introductionmentioning

confidence: 74%

[Retracted] Evaluation of Mechanical Properties of Materials Based on Genetic Algorithm Optimizing BP Neural Network

Liu

Guang-ping

2021

Computational Intelligence and Neuroscience

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In the 21st century, with the increasingly urgent requirements for lightweight in the fields of aviation, aerospace, and electronics, especially automobiles, many properties of magnesium alloy materials, especially the low-density performance characteristics, have been widely valued. In order to better use magnesium metal materials, it is very important to evaluate its mechanical properties. This article is based on 196 sets of mechanical performance experimental results and related data of AZ31 and AZ91 2 magnesium alloys. Based on data analysis and sorting, take deformation temperature, deformation rate, deformation coefficient, solid solution temperature, and solid solution time as input and take ultimate tensile strength (UTS), yield strength (YS), and elongation (ELO) as output. The 5-8-1 three-layer BP neural network forecast model optimized by the genetic algorithm is used for data training. The training results show that the prediction model can accurately predict the tensile strength, yield strength, and elongation. Compared with the general BP neural network prediction model, the BP neural network based on the genetic algorithm has small discreteness and high fitness: the average error of UTS and YS of AZ31 magnesium alloy is reduced to 0.88% and 3.3%, respectively. The most obvious is that the elongation of AZ31 ELO is reduced, and the error is reduced to 8.1%.

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Section: Introductionmentioning

confidence: 74%

[Retracted] Evaluation of Mechanical Properties of Materials Based on Genetic Algorithm Optimizing BP Neural Network

Liu

Guang-ping

2021

Computational Intelligence and Neuroscience

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“…The artificial neural network (ANN) is a multi-dimensional information space that can learn information patterns. ANN exhibits strong computation intelligence, which can predict any complex and non-linear system [117]- [119] ANN has the advantage to manage and control several types of problems with its improved learning ability and without depending on the mathematical functional relationship [120], [121]. ANN is employed in building energy management scheduling controller, [122], as shown in Fig.…”

Section: ) Artificial Neural Network Controlmentioning

confidence: 99%

Intelligent Controllers and Optimization Algorithms for Building Energy Management Towards Achieving Sustainable Development: Challenges and Prospects

et al. 2021

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Buildings account for a significant amount of energy consumption leading to the issues of global emissions and climate change. Thus, energy management in a building is increasingly explored due to its significant potential in reducing the overall electricity expenses for the consumers and mitigating carbon emissions. In line with that, the greater control and optimization of energy management integrated with renewable energy resources is required to improve building energy efficiency while satisfying indoor environment comfort. Even though actions are being taken to reduce the energy consumption in buildings with several optimization and controller techniques, yet some issues remain unsolved. Therefore, this work provides a comprehensive review of the conventional and intelligent control methods with emphasis on their classification, features, configuration, benefits, and drawbacks. This review critically investigates the different optimization objectives and constraints with respect to comfort management, energy consumption, and scheduling. Furthermore, the review outlines the different methodological approaches to optimization algorithms used in building energy management. The contributions of controller and optimization in building energy management with the relation of sustainable development goals (SDGs) are explained rigorously. Discussions on the key challenges of the existing methods are presented to identify the gaps for future research. The review delivers some effective future directions that would be beneficial to the researchers and industrialists to design an efficiently optimized controller for building energy management toward targeting SDGs.INDEX TERMS Building energy management, controller, optimization, scheduling, sustainable development goals I. INTRODUCTIONPresently, buildings take the lead in consuming a substantial amount of energy, indicating about 40% of global energy consumption, which is responsible to release one-third of greenhouse gas (GHG) emissions [1], [2]. Another report demonstrates that buildings hold 49% of the total energy worldwide in which 60% of the energy is consumed for heating and cooling purposes [3], [4]. The poor management and ineffective control approach of appliances used in the building may result in a significant loss of energy in a

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“…However, if the wind power output is lower than ten megawatts, the proposed model's performance is inferior to the individual models. Ilbeigi et al [10] performed research to reduce energy consumption in Iran. They trained and employed numerous artificial neural network (ANN) models, assessed the power required by the office, evaluated the impact of the energy factor, and performed a comprehensive sensitivity analysis to find the most effective model.…”

Section: Related Workmentioning

confidence: 99%

Genetic Algorithm Based Optimized Feature Engineering and Hybrid Machine Learning for Effective Energy Consumption Prediction

Byun

2020

IEEE Access

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Smart grids are developing rapidly, leading to the need for accurate forecasts of power consumption. However, developing a precise time series model for energy forecasting is difficult. It has to be trained using optimal meteorological features such as temperature and time lags to qualify for a beneficial model. We have proposed an approach that uses an ensemble machine learning model based on XGBoost, support vector regressor (SVR), and K-nearest neighbors (KNN) regressor algorithms. We have also used the genetic algorithm (GA) to predict total load consumption from optimal feature selection. Using Jeju island's electricity consumption data as a case study shows that the proposed ensemble model optimized with GA is more accurate than the individual machine learning models. Using only the best-selected weather and time features, the proposed model records all the features of a complicated time series and shows a reduction in the mean absolute percentage error (MAPE) and the root mean square log error for the week ahead forecasts. We got 3.35 % MAPE of the three months test data by applying the proposed model. The smart grids operators can manage resources effectively to provide excellent services to the consumers based on the recommended model outcomes.

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Prediction and optimization of energy consumption in an office building using artificial neural network and a genetic algorithm

Cited by 194 publications

References 45 publications

[Retracted] Evaluation of Mechanical Properties of Materials Based on Genetic Algorithm Optimizing BP Neural Network

[Retracted] Evaluation of Mechanical Properties of Materials Based on Genetic Algorithm Optimizing BP Neural Network

Intelligent Controllers and Optimization Algorithms for Building Energy Management Towards Achieving Sustainable Development: Challenges and Prospects

Genetic Algorithm Based Optimized Feature Engineering and Hybrid Machine Learning for Effective Energy Consumption Prediction

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