Application of Particle Swarm Optimization Algorithm in the Heating System Planning Problem

Ma, Rongjiang; Yu, Nanyang; Hu, Jinglu

doi:10.1155/2013/718345

Cited by 32 publications

(15 citation statements)

References 17 publications

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“…In this study, particle swarm optimization (PSO) method is presented to determine the optimal parameters of the MLR model. The PSO methods have proven to be very effective in solving a variety of difficult global optimization problems in forecasting (Chen and Kao [ 21 ] and Alwee et al [ 22 ]), heat problem (Ma et al [ 23 ] and Tyagi and Pandit [ 24 ]), and dynamic environments (Liu et al [ 25 ]).…”

Section: Methodsmentioning

confidence: 99%

Crude Oil Price Forecasting Based on Hybridizing Wavelet Multiple Linear Regression Model, Particle Swarm Optimization Techniques, and Principal Component Analysis

Shabri

Samsudin

2014

The Scientific World Journal

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Crude oil prices do play significant role in the global economy and are a key input into option pricing formulas, portfolio allocation, and risk measurement. In this paper, a hybrid model integrating wavelet and multiple linear regressions (MLR) is proposed for crude oil price forecasting. In this model, Mallat wavelet transform is first selected to decompose an original time series into several subseries with different scale. Then, the principal component analysis (PCA) is used in processing subseries data in MLR for crude oil price forecasting. The particle swarm optimization (PSO) is used to adopt the optimal parameters of the MLR model. To assess the effectiveness of this model, daily crude oil market, West Texas Intermediate (WTI), has been used as the case study. Time series prediction capability performance of the WMLR model is compared with the MLR, ARIMA, and GARCH models using various statistics measures. The experimental results show that the proposed model outperforms the individual models in forecasting of the crude oil prices series.

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Section: Methodsmentioning

confidence: 99%

Crude Oil Price Forecasting Based on Hybridizing Wavelet Multiple Linear Regression Model, Particle Swarm Optimization Techniques, and Principal Component Analysis

Shabri

Samsudin

2014

The Scientific World Journal

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“…Matlab R2018a [38] was used in this optimisation problem with PSO algorithm being employed in the optimisation problem to minimise the unit energy cost C e . This algorithm has capabilities for both local and global optimisation thus it is capable of retaining the global solution in the search space [39,40]. Owing to its ability to retain the global solution, it has been successfully implemented in minimising the cost per unit power for a wind farm [41] and hence the global version of PSO was used in this study.…”

Section: Modelling and Optimisationmentioning

confidence: 99%

Cleaning cycle optimisation in non-tracking ground mounted solar PV systems using Particle Swarm Optimisation

Chiteka

Arora

Sridhara

et al. 2020

Int. J. Simul. Multidisci. Des. Optim.

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The effect of installation azimuth angle in the optimization of the cleaning cycle of a solar photovoltaic plant was experimentally investigated in this study. The optimum cleaning cycle was determined using Particle Swarm Optimization algorithm cognizance of the fact that different orientations have different soiling rates. Soiling rates on three different azimuth configurations were experimentally investigated and an exponential soiling loss model was developed for each configuration for use in the optimization problem. Azimuth angle differences of ±12.5% were found to have a significant influence on soiling of as much as 28.29% for the selected location. The North of North West configuration was found to be optimal as opposed to the generally accepted North configuration for maximum energy generation at a minimum cost of energy. This configuration generated 0.87% more energy at unit energy cost of $0.093 compared to the North configuration which had a minimum cost of $0.113. The optimized cleaning cycles were 35 days for the optimal configuration while the North configuration had an optimized cleaning cycle of 28 days. A 17.7% difference in the cost of energy was recorded due the influence of soiling. The study revealed that for minimizing the unit energy cost, it is necessary to take into effect the influence of soiling.

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“…Ma et al presented a mathematical model and improved the PSO algorithm for solving the DHS planning problem. This model includes an objective function that expresses the minimum cost of a DHS for a given life cycle time.…”

Section: Overview Of the Optimisation Models In The Literaturementioning

confidence: 99%

A review of modelling and optimisation techniques for district heating systems

2019

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Summary This article provides an extensive survey on the modelling and optimisation of district heating systems (DHSs) focused on the heat distribution network. For this purpose, the major components of a DHS have been described and discussed, and their modelling has been briefly reviewed, including numerical simulation models for heat sources, end‐users, and especially the distribution network. The main deterministic and heuristic optimisation techniques are briefly described. Additionally, a single‐objective and multiobjective optimisation problem is generally formulated, and the main optimisation criteria for the design and operation of the distribution network are synthesised and analysed. The state of the art in DHS optimisation has been also reviewed and categorised. Finally, some recommendations for future developments were included.

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Application of Particle Swarm Optimization Algorithm in the Heating System Planning Problem

Cited by 32 publications

References 17 publications

Crude Oil Price Forecasting Based on Hybridizing Wavelet Multiple Linear Regression Model, Particle Swarm Optimization Techniques, and Principal Component Analysis

Crude Oil Price Forecasting Based on Hybridizing Wavelet Multiple Linear Regression Model, Particle Swarm Optimization Techniques, and Principal Component Analysis

Cleaning cycle optimisation in non-tracking ground mounted solar PV systems using Particle Swarm Optimisation

A review of modelling and optimisation techniques for district heating systems

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