Combination forecasting model of equipment and material prices for power grid production technological transformation projects based on unary linear regression and grey theory

Impact loads in large iron and steel enterprise bring the power system reactive power impact, which makes the fluctuation of the system voltage, power factor and other parameters are out of the limitation of the national standard. Substation bus reactive load forecasting in large iron and steel enterprise can be introduced to determine reactive power optimization strategy and the switching of capacitors. In this paper, a combination forecasting model of quadratic self-adaptive exponential smoothing (QSES) model and converse exponential (CE) model has been proposed for substation bus reactive load forecasting. The numerical results in Jinan iron and steel Group show the application of this model is encouraging. Introduction

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A Combination Forecasting Model for Fast Cost Estimating in Civil Engineering

Proceedings of the 17th International Symposium on Advancement of Construction Management and Real Estate ◽

10.1007/978-3-642-35548-6_120 ◽

2013 ◽

pp. 1183-1190

Author(s):

Xun Liang

Keyword(s):

Civil Engineering ◽

Forecasting Model ◽

Cost Estimating ◽

Combination Forecasting ◽

Combination Forecasting Model

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Short-Term Photovoltaic Power Generation Combination Forecasting Method Based on Similar Day and Cross Entropy Theory

International Journal of Photoenergy ◽

10.1155/2018/6973297 ◽

2018 ◽

Vol 2018 ◽

pp. 1-10 ◽

Cited By ~ 8

Author(s):

Qi Wang ◽

Shunxiang Ji ◽

Minqiang Hu ◽

Wei Li ◽

Fusuo Liu ◽

...

Keyword(s):

Power Generation ◽

Least Square ◽

Cross Entropy ◽

Support Vector ◽

Forecasting Model ◽

Entropy Theory ◽

Short Term ◽

Combination Forecasting ◽

Combination Forecasting Model ◽

Similar Day

The forecast for photovoltaic (PV) power generation is of great significance for the operation and control of power system. In this paper, a short-term combination forecasting model for PV power based on similar day and cross entropy theory is proposed. The main influencing factors of PV power are analyzed. From the perspective of entropy theory, considering distance entropy and grey relation entropy, a comprehensive index is proposed to select similar days. Then, the least square support vector machine (LSSVM), autoregressive and moving average (ARMA), and back propagation (BP) neural network are used to forecast PV power, respectively. The weights of three single forecasting methods are dynamically set by the cross entropy algorithm and the short-term combination forecasting model for PV power is established. The results show that this method can effectively improve the prediction accuracy of PV power and is of great significance to real-time economical dispatch.

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Aviation Oil Demand Combination Forecasting Based on Vectorial Angle Cosine

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.706-708.1989 ◽

2013 ◽

Vol 706-708 ◽

pp. 1989-1993

Author(s):

Jun Wang ◽

Zhi Hong Sun ◽

Li Zhang

Keyword(s):

Forecasting Model ◽

Effect Evaluation ◽

Oil Consumption ◽

Model Based ◽

Minimum Value ◽

Combination Forecasting ◽

Oil Demand ◽

Combination Forecasting Model ◽

The Individual

According to the individual forecasting of aviation oil consumption, with taking the minimum value of the angle between the actual value vector and the predicted value vector as a target, we established a combination forecasting model based on the vectorial angle cosine. Through the analysis of an actual example, from the perspective of the effect evaluation indicators of prediction reflect that this combination forecasting model is advantage compared to each single forecasting model.

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