Scenario Analysis of City Residential Electricity Consumption Based on the Time-Series Forecast - Taking Quanzhou City as an Example

2012 ◽  
Vol 178-181 ◽  
pp. 184-188
Author(s):  
Xiang Chao Hou ◽  
Lu Jie Zhu
Keyword(s):  
Time Series ◽  
Electricity Consumption ◽  
Prediction Method ◽  
Residential Building ◽  
Building Energy Efficiency ◽  
Effective Measure ◽  
Time Series Forecast ◽  
Development Scenarios ◽  
The Future ◽  
Residential Electricity

In this paper, the development scenarios of electricity consumption of city residents living were analyzed by using the prediction method of time-series smoothing. The simulation calculation to the future development scenarios of electricity consumption forecasts the electricity consumption values of residential building in this case area from 2010 to 2050. The conclusion, that controlling living area per person is most effective measure, has reference significance for the future residential building energy efficiency work.

Trigonometric Grey Prediction Method for Turkey's Electricity Consumption Prediction

2021 ◽  
pp. 136-154
Author(s):  
Adem Tuzemen
Keyword(s):  
Evaluation Criteria ◽  
Electricity Consumption ◽  
Prediction Method ◽  
Electricity Demand ◽  
Grey Prediction ◽  
Data Set ◽  
Determining Factors ◽  
The Future ◽  
Forecasting Analysis ◽  
Short Term Forecasting

Industry and technology continue to develop rapidly in today's world. The indisputable most important source of this development, energy is among the indispensables of daily life. Since it is one of the determining factors for the country's economy, the future forecast of electricity demand means calculating the future steps. Based on this, to forecast Turkey's electricity demand, it was benefited from grey model (GM) and trigonometric GM (TGM) techniques. The data set includes annual electricity consumption for the period 1970-2018. The performances of the methods determined were compared based on the forecast evaluation criteria (MSE, MAD, MAPE, and RMSE). Short-term forecasting analysis was carried out by determining the method that gives these values to a minimum. In the future forecast, it has been determined that electricity consumption will increase continuously.

scholarly journals NUMERICAL COMPUTATION AND PREDICTION OF ELECTRICITY CONSUMPTION IN TOBACCO INDUSTRY

2017 ◽  
Vol 15 (3) ◽  
pp. 457
Author(s):  
Mirjana Laković ◽  
Ivan Pavlović ◽  
Miloš Banjac ◽  
Milica Jović ◽  
Marko Mančić
Keyword(s):  
Time Series ◽  
Energy Consumption ◽  
Autoregressive Model ◽  
Moving Average ◽  
Electricity Consumption ◽  
Ar Model ◽  
Model Complexity ◽  
Industrial Plant ◽  
Time Series Models ◽  
The Future

Electricity is a key energy source in each country and an important condition for economic development. It is necessary to use modern methods and tools to predict energy consumption for different types of systems and weather conditions. In every industrial plant, electricity consumption presents one of the greatest operating costs. Monitoring and forecasting of this parameter provide the opportunity to rationalize the use of electricity and thus significantly reduce the costs. The paper proposes the prediction of energy consumption by a new time-series model. This involves time series models using a set of previously collected data to predict the future load. The most commonly used linear time series models are the AR (Autoregressive Model), MA (Moving Average) and ARMA (Autoregressive Moving Average Model). The AR model is used in this paper. Using the AR (Autoregressive Model) model, the Monte Carlo simulation method is utilized for predicting and analyzing the energy consumption change in the considered tobacco industrial plant. One of the main parts of the AR model is a seasonal pattern that takes into account the climatic conditions for a given geographical area. This part of the model was delineated by the Fourier transform and was used with the aim of avoiding the model complexity. As an example, the numerical results were performed for tobacco production in one industrial plant. A probabilistic range of input values is used to determine the future probabilistic level of energy consumption.

scholarly journals Dynamic Prediction for Accuracy Maintaining Reliability of Superprecision Rolling Bearing in Service

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Liang Ye ◽  
Xintao Xia ◽  
Zhen Chang
Keyword(s):  
Time Series ◽  
Bootstrap Method ◽  
Prediction Method ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Grey System Theory ◽  
Dynamic Monitoring ◽  
Grey System ◽  
Dynamic Prediction ◽  
The Future

A dynamic prediction method for accuracy maintaining reliability (AMR) of superprecision rolling bearings (SPRBs) in service is proposed by effectively fusing chaos theory and grey system theory and applying stochastic processes. In this paper, the time series of a vibration signal is used to characterize the state information for SPRB, and four runtime data points can be predicted in the future, which depends on four chaotic forecasting models to preprocess the time series. Using the grey bootstrap method and sampling from the four runtime data, a large amount of generated data (GD) are gained to analyze the changes in information on bearing service accuracy. Then, using a predefined accuracy threshold to match the Poisson count for the GD, the estimated value of variation intensity is obtained. Subsequently, with the help of the Poisson process, the dynamic evolution process is forecast in real time for AMR of the SPRB for each step in the future. Finally, according to a novel concept for maintaining relative reliability in an SPRB, the failure degree of a bearing maintaining an optimum accuracy status (BMOAS) is effectively described. Experimental investigation shows that multiple chaotic forecasting methods are accurate and feasible with all relative errors below 15%; the reliability of each step in the future can truly be described, and the prediction results for AMR over the same subseries show good consistency; dynamic monitoring of the health status of SPRB can be realized by the degree to which a BMOAS fails.

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