A probabilistic model for latent least squares regression

Wang, Shengzheng; Yang, Jie

doi:10.1016/j.neucom.2014.09.014

Cited by 4 publications

(3 citation statements)

References 19 publications

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“…Given a set of training samples, LSR can find the coefficients of a linear model by minimizing the residual sum of squares [57]. For LSR method, the electricity consumption data points from 2001 to 2009 of Inner Mongolia will be treated as the training samples.…”

Section: Comparison Of Forecasting Results By Different Forecasting Mmentioning

confidence: 99%

Using GM (1,1) Optimized by MFO with Rolling Mechanism to Forecast the Electricity Consumption of Inner Mongolia

Zhao

Guo

2016

Applied Sciences

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Accurate and reliable forecasting on annual electricity consumption will be valuable for social projectors and power grid operators. With the acceleration of electricity market reformation and the development of smart grid and the energy Internet, the modern electric power system is becoming increasingly complex in terms of structure and function. Therefore, electricity consumption forecasting has become a more difficult and challenging task. In this paper, a new hybrid electricity consumption forecasting method, namely grey model (1,1) (GM (1,1)), optimized by moth-flame optimization (MFO) algorithm with rolling mechanism (Rolling-MFO-GM (1,1)), was put forward. The parameters a and b of GM (1,1) were optimized by employing moth-flame optimization algorithm (MFO), which is the latest natured-inspired meta-heuristic algorithm proposed in 2015. Furthermore, the rolling mechanism was also introduced to improve the precision of prediction. The Inner Mongolia case discussion shows the superiority of proposed Rolling-MFO-GM (1,1) for annual electricity consumption prediction when compared with least square regression (LSR), GM (1,1), FOA (fruit fly optimization)-GM (1,1), MFO-GM (1,1), Rolling-LSR, Rolling-GM (1,1) and Rolling-FOA-GM (1,1). The grey forecasting model optimized by MFO with rolling mechanism can improve the forecasting performance of annual electricity consumption significantly.

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Section: Comparison Of Forecasting Results By Different Forecasting Mmentioning

confidence: 99%

Using GM (1,1) Optimized by MFO with Rolling Mechanism to Forecast the Electricity Consumption of Inner Mongolia

Zhao

Guo

2016

Applied Sciences

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“…Drawing on the wavelet neural network (WNN), Wang et al [23] established a model to optimize energy efficiency in real time and used the model to determine the optimal engine speeds based on the data collected from GPS receiver, wind speed sensor, water depth sensor, and other technologies. In addition, some relevant regression approaches were developed by Lepore et al [24], Wang and Yang [25], and Wang et al [23]. For example, Wang et al [26] discovered the close correlations between various eigenvariables that affect fuel consumption and selected these eigenvariables with the LASSO regression algorithm.…”

Section: Literature Reviewmentioning

confidence: 99%

Multiobjective Route Selection Based on LASSO Regression: When Will the Suez Canal Lose Its Importance?

Zhou

Zhao

Liang

2021

Mathematical Problems in Engineering

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With coronavirus disease 2019 reshaping the global shipping market, many ships in the Europe-Asia trades that need to sail through the Suez Canal begun to detour via the much longer route, the Cape of Good Hope. In order to explain and predict the route choice, this paper employs the least absolute shrinkage and selection operator regression to estimate fuel consumption based on the automatic identification system and ocean dataset and designed a multiobjective particle swarm optimization to find Pareto optimal solutions that minimize the total voyage cost and total voyage time. After that, the weighted sum method was introduced to deal with the route selection. Finally, a case study was conducted on the real data from CMA CGM, a leading worldwide shipping company, and four scenarios of fuel prices and charter rates were built and analyzed. The results show that the detour around the Cape of Good Hope is preferred only in the scenario of low fuel price and low charter. In addition, the paper suggests that the authority of Suez Canal should cut down the canal toll according to our result to win back the ships because we have verified that offering a discount on the canal roll is effective.

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“…The iteration begins with programming the original parameters of FOA: 10,10]. For the OLS model, through minimizing the residual sum of squares, the OLS model can calculate the coefficients of a linear model [33]. For these selected models, just like that of WOA-LSSVM model, the training sample ranges from 1991 to 2010, and the data between 2011 and 2014 are treated as the testing sample.…”

Section: Selection Of Comparison Models and Forecasting Performance Ementioning

confidence: 99%

Energy-Related CO2 Emissions Forecasting Using an Improved LSSVM Model Optimized by Whale Optimization Algorithm

2017

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Accurate and reliable forecasting on energy-related carbon dioxide (CO 2) emissions is of great significance for climate policy decision making and energy planning. Due to the complicated nonlinear relationships of CO 2 emissions with its driving forces, the accurate forecasting for CO 2 emissions is a tedious work, which is an important issue worth studying. In this study, a novel CO 2 emissions prediction method is proposed which employs the latest nature-enlightened optimization method, named the Whale optimization algorithm (WOA), to search the optimized values of two parameters of LSSVM (least squares support vector machine), namely the WOA-LSSVM model. Meanwhile, the driving forces of CO 2 emissions including GDP (gross domestic product), energy consumption and population are chosen to be the import variables of the proposed WOA-LSSVM method. Taking China's CO 2 emissions as an instance, the effectiveness of WOA-LSSVM-based CO 2 emissions forecasting is verified. The comparative analysis results indicate that the WOA-LSSVM model is significantly superior to other selected models, namely FOA (fruit fly optimization algorithm)-LSSVM, LSSVM, and OLS (ordinary least square) models in terms of CO 2 emissions forecasting. The proposed WOA-LSSVM model has the potential to effectively improve the accuracy of CO 2 emissions forecasting. Meanwhile, as a new nature-enlightened heuristic optimization algorithm, the WOA has the prospect for wide application.

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A probabilistic model for latent least squares regression

Cited by 4 publications

References 19 publications

Using GM (1,1) Optimized by MFO with Rolling Mechanism to Forecast the Electricity Consumption of Inner Mongolia

Using GM (1,1) Optimized by MFO with Rolling Mechanism to Forecast the Electricity Consumption of Inner Mongolia

Multiobjective Route Selection Based on LASSO Regression: When Will the Suez Canal Lose Its Importance?

Energy-Related CO2 Emissions Forecasting Using an Improved LSSVM Model Optimized by Whale Optimization Algorithm

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