A Combined Power Demand Forecasting Model with Variable Weight

2013 ◽  
Vol 392 ◽  
pp. 618-621
Author(s):  
Zhi Gang Wang ◽  
Qing Jie Zhou ◽  
Xing Hua Zhou
Keyword(s):  
Demand Forecasting ◽  
Development Trend ◽  
Forecasting Model ◽  
Power Demand ◽  
Proposed Model ◽  
Variable Weight ◽  
The Difference ◽  
Demand Forecasting Model ◽  
Analysis Models ◽  
The Impact

A combined power demand forecasting model with variable weight considering both of the impact of the macroeconomic situation and the internal development trend is proposed. The proposed model consists of regression analysis models and the trend extrapolation models. The variable weight is determined by the difference of the prediction results between the two kinds of models . Beijing's power demand forecasting illustrates the usefulness and reliability of the combined model.

scholarly journals Shipbuilding capacity optimization using shipbuilding demand forecasting model

2021 ◽  
Author(s):  
Yujiro Wada ◽  
Kunihiro Hamada ◽  
Noritaka Hirata
Keyword(s):  
Supply And Demand ◽  
Demand Forecasting ◽  
Sustainable Growth ◽  
Forecasting Model ◽  
Demand Forecast ◽  
Shipbuilding Industry ◽  
Complex Issue ◽  
Proposed Model ◽  
Demand Fluctuations ◽  
Demand Forecasting Model

AbstractThe shipbuilding industry has been drastically affected by demand fluctuations. Currently, it faces intense global competition and a crisis because of an imbalance between supply and demand. This imbalance of supply and demand is caused by an excess of shipbuilding capacity. The Organisation for Economic Co-operation and Development has considered adjusting the shipbuilding capacity to reduce the imbalance based on the demand forecast. On the other hand, demand forecast of shipbuilding is a complex issue because the demand is influenced indirectly by adjustments in shipbuilding capacity. Therefore, it is important to examine the influence of construction capacity adjustments on the future demand of ships based on demand forecasting for the sustainable growth of the shipbuilding industry. In this study, shipbuilding capacity adjustment is considered using a proposed simulation system based on a demand-forecasting model. Additionally, the system dynamics model of a previous study is improved by developing a ship price-prediction model for evaluating the shipbuilding capacity-adjustment scenario. We conduct simulations using the proposed demand-forecasting model and system to confirm the effectiveness of the proposed model and system. Furthermore, several shipbuilding capacity-adjustment scenarios are discussed using the proposed system.

Demand Forecasting Model for Park-and-Ride Lots in King County, Washington

1998 ◽  
Vol 1623 (1) ◽  
pp. 80-87 ◽  
Author(s):  
Susan Hendricks ◽  
Maren Outwater
Keyword(s):  
Stated Preference ◽  
Demand Forecasting ◽  
User Fees ◽  
Forecasting Model ◽  
King County ◽  
Capacity Enhancement ◽  
Park And Ride ◽  
Simplifying Assumptions ◽  
Demand Forecasting Model ◽  
The Impact

As the demand for using park-and-ride lots grows, the need to accurately forecast these trips also grows. Initially, demand for park-and-ride lots was forecast using a technique that identified the draw area for each lot and estimated demand without regard to capacity. These were simplifying assumptions that are no longer appropriate with respect to current demand for park-and-ride lots. In King County, Washington, the 12 largest park-and-ride lots are currently operating at 95 percent utilization. According to a recent park-and-ride lot survey in King County, there is significant latent demand for using lots that are full. The analysis of demand for parking in park-and-ride lots in King County was developed as part of the Washington State Department of Transportation Public/Private Partnership Program for the Park-and-Ride Capacity Enhancement Project. There were 17 park-and-ride lots considered for capacity enhancement. The approach to evaluate park-and-ride lot demand uses a technique to identify intermediate stop choices, such as park-and-ride lots, as part of the overall modal choice. User and nonuser surveys were evaluated to identify the importance of specific variables, such as security or amenities. A combination of the survey results and an application of the demand forecasting model were used to estimate shifts in demand for parking from increased capacity and user fees. Several different user fees were tested and compared with stated preference results from the user and nonuser surveys. The project resulted in significantly different demand for park and ride than previous modeling efforts because of the impact of lot capacity and effects of user fees.

Logistics demand forecasting model based on improved neural network algorithm

2020 ◽  
pp. 1-11
Author(s):  
Hongjiang Ma ◽  
Xu Luo
Keyword(s):  
Neural Network ◽  
Influencing Factor ◽  
Demand Forecasting ◽  
Forecasting Model ◽  
Logistics System ◽  
Model Based ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Demand Forecasting Model ◽  
Logistics Demand

The irrationality between the procurement and distribution of the logistics system increases unnecessary circulation links and greatly reduces logistics efficiency, which not only causes a waste of transportation resources, but also increases logistics costs. In order to improve the operation efficiency of the logistics system, based on the improved neural network algorithm, this paper combines the logistic regression algorithm to construct a logistics demand forecasting model based on the improved neural network algorithm. Moreover, according to the characteristics of the complexity of the data in the data mining task itself, this article optimizes the ladder network structure, and combines its supervisory decision-making part with the shallow network to make the model more suitable for logistics demand forecasting. In addition, this paper analyzes the performance of the model based on examples and uses the grey relational analysis method to give the degree of correlation between each influencing factor and logistics demand. The research results show that the model constructed in this paper is reasonable and can be analyzed from a practical perspective.

scholarly journals Usage of the Pareto Fronts as a Tool to Select Data in the Forecasting Process—A Short-Term Electric Energy Demand Forecasting Case

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3204
Author(s):  
Michał Sabat ◽  
Dariusz Baczyński
Keyword(s):  
Energy Demand ◽  
Demand Forecasting ◽  
Electric Energy ◽  
Forecasting Model ◽  
Short Term ◽  
Complex Evaluation ◽  
Proposed Model ◽  
Energy Demand Forecasting ◽  
Pareto Fronts ◽  
Naive Approach

Transmission, distribution, and micro-grid system operators are struggling with the increasing number of renewables and the changing nature of energy demand. This necessitates the use of prognostic methods based on ever shorter time series. This study depicted an attempt to develop an appropriate method by introducing a novel forecasting model based on the idea to use the Pareto fronts as a tool to select data in the forecasting process. The proposed model was implemented to forecast short-term electric energy demand in Poland using historical hourly demand values from Polish TSO. The study rather intended on implementing the range of different approaches—scenarios of Pareto fronts usage than on a complex evaluation of the obtained results. However, performance of proposed models was compared with a few benchmark forecasting models, including naïve approach, SARIMAX, kNN, and regression. For two scenarios, it has outperformed all other models by minimum 7.7%.

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