scholarly journals A Novel Decomposition and Combination Technique for Forecasting Monthly Electricity Consumption

2021 ◽  
Vol 9 ◽  
Author(s):  
Xi Zhang ◽  
Rui Li
Keyword(s):  
Electricity Consumption ◽  
Forecast Model ◽  
Time Correlation ◽  
Normal Operation ◽  
Economic Activities ◽  
Forecasting Accuracy ◽  
Combination Technique ◽  
Residual Component ◽  
Seasonal Characteristics ◽  
Monthly Electricity Consumption

With the share of electricity in total final energy consumption increasing quickly, the world is becoming increasingly dependent on electricity, which makes it more and more important to improve the forecasting accuracy of electricity consumption to ensure the normal operation of economic activities. In this paper, a novel decomposition and combination technique to forecast monthly electricity consumption is proposed. First, we use STL decomposition to obtain the trend, season, and residual components of the time series. Second, we use SARIMA, SVR, ANN, and LSTM to forecast trend, season, and residual component, respectively. Third, we use time correlation principle to improve the forecasting accuracy of season component. Fourth, we integrated the residual component predicted by SARIMA, SVR, ANN, and LSTM into a new sequence to improve the forecasting accuracy of residual component. In order to verify the performance of the proposed forecast model, monthly electricity consumption data in China is introduced as an example for empirical analysis. The results show that after STL decomposition, time correlation modification, and residual modification, the forecasting accuracy of each model has been gradually improved. We believe that the proposed forecast model in this paper can also be used to solve other mid- and long-term forecasting problems with obvious seasonal characteristics.

Study on Forecasting Model of Monthly Electricity Consumption Based on Kernel Partial Least-Squares and Exponential Smoothing Method

2013 ◽  
Vol 805-806 ◽  
pp. 1221-1227
Author(s):  
Hai Yan Wang ◽  
Shi Jun Chen
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Electricity Market ◽  
Electricity Consumption ◽  
Forecast Model ◽  
Exponential Smoothing ◽  
Least Squares Regression ◽  
Kernel Partial Least Squares ◽  
Cyclical Trend ◽  
Monthly Electricity Consumption

it is very necessary for electricity market operation to accurate forecasting monthly electricity consumption, influencing factors of electricity consumption, there are non-linear and strong correlation, taking into account the cyclical trend of the monthly electricity consumption, this paper raises a monthly electricity consumption forecast model based on kernel partial least squares and exponential smoothing regression. The forecast model is the first to use kernel partial least squares regression methods to predict the annual electricity consumption, and then combined with exponential smoothing obtained monthly electricity accounts for the proportion of electricity consumption throughout the year for each month of the year to be measured power consumption . Instance analysis and calculation results show that the method has higher prediction accuracy, good practicality and feasibility.

Monthly Electricity Consumption Forecasting: A Step-Reduction Strategy and Autoencoder Neural Network

2020 ◽  
pp. XX10-XX10
Author(s):  
Zhenghui Li ◽  
Kangping Li ◽  
Fei Wang ◽  
Zhiming Xuan ◽  
Zengqiang Mi ◽  
...  
Keyword(s):  
Neural Network ◽  
Electricity Consumption ◽  
Reduction Strategy ◽  
Monthly Electricity Consumption

Modeling CO2 Emissions of Top 5 Emitters with Combination Technique

2012 ◽  
Vol 616-618 ◽  
pp. 1143-1147
Author(s):  
Wei Sun ◽  
Jing Min Wang ◽  
Jun Jie Kang
Keyword(s):  
Forecast Error ◽  
Combination Method ◽  
Future Projection ◽  
Forecasting Accuracy ◽  
Combination Technique ◽  
Combination Forecast ◽  
Forecast Models ◽  
Model Time Series ◽  
Grey Verhulst Model ◽  
Better Than

In this paper, the performance of combination forecast methods for CO2 emissions prediction is investigated. Linear model, time series model, GM (1, 1) model and Grey Verhulst model are selected in study as the separate models. And, four kinds of combination forecast models, i.e. the equivalent weight (EW) combination method, variance-covariance (VACO) combination method, regression combination (R) method, and discounted mean square forecast error (MSFE) method are chosen to employ for top 5 CO2 emitters. The forecasting accuracy is compared between these combination models and single models. This research suggests that the combination forecasts are almost certain to outperform the worst individual forecasts and maybe even better than most individual ones. Furthermore the combination forecasts can avoid the risk of model choosing in future projection. For CO2 emissions forecast with many uncertain factors in the future, combining the single forecast would be safer in such forecasting situations.

scholarly journals Dynamics of Electricity Demand in Lesotho: A Kalman Filter Approach

2015 ◽  
Vol 10 (1) ◽  
pp. 130-139 ◽  
Author(s):  
Retselisitsoe Isaiah Thamae ◽  
Leboli Zachia Thamae ◽  
Thimothy Molefi Thamae
Keyword(s):  
Kalman Filter ◽  
Electricity Consumption ◽  
Electricity Demand ◽  
International Standards ◽  
Economic Activities ◽  
Electricity Prices ◽  
Income Elasticities ◽  
Price And Income Elasticities ◽  
Long Run ◽  
Filter Approach

Abstract This study provides an empirical analysis of the time-varying price and income elasticities of electricity demand in Lesotho for the period 1995-2012 using the Kalman filter approach. The results reveal that economic growth has been one of the main drivers of electricity consumption in Lesotho while electricity prices are found to play a less significant role since they are monopoly-driven and relatively low when compared to international standards. These findings imply that increases in electricity prices in Lesotho might not have a significant impact on consumption in the short-run. However, if the real electricity prices become too high over time, consumers might change their behavior and sensitivity to price and hence, energy policymakers will need to reconsider their impact in the long-run. Furthermore, several exogenous shocks seem to have affected the sensitivity of electricity demand during the period prior to regulation, which made individuals, businesses and agencies to be more sensitive to electricity costs. On the other hand, the period after regulation has been characterized by more stable and declining sensitivity of electricity demand. Therefore, factors such as regulation and changes in the country’s economic activities appear to have affected both price and income elasticities of electricity demand in Lesotho.

scholarly journals Monthly Electricity Consumption Forecasting Method Based on X12 and STL Decomposition Model in an Integrated Energy System

2019 ◽  
Vol 2019 ◽  
pp. 1-16
Author(s):  
Tianhe Sun ◽  
Tieyan Zhang ◽  
Yun Teng ◽  
Zhe Chen ◽  
Jiakun Fang
Keyword(s):  
Power Consumption ◽  
Rapid Development ◽  
Electricity Consumption ◽  
Energy System ◽  
Small Scale ◽  
Power Demand ◽  
Distributed Energy ◽  
Strategic Competition ◽  
Integrated Energy System ◽  
Monthly Electricity Consumption

With the rapid development and wide application of distributed generation technology and new energy trading methods, the integrated energy system has developed rapidly in Europe in recent years and has become the focus of new strategic competition and cooperation among countries. As a key technology and decision-making approach for operation, optimization, and control of integrated energy systems, power consumption prediction faces new challenges. The user-side power demand and load characteristics change due to the influence of distributed energy. At the same time, in the open retail market of electricity sales, the forecast of electricity consumption faces the power demand of small-scale users, which is more easily disturbed by random factors than by a traditional load forecast. Therefore, this study proposes a model based on X12 and Seasonal and Trend decomposition using Loess (STL) decomposition of monthly electricity consumption forecasting methods. The first use of the STL model according to the properties of electricity each month is its power consumption time series decomposition individuation. It influences the factorization of monthly electricity consumption into season, trend, and random components. Then, the change in the characteristics of the three components over time is considered. Finally, the appropriate model is selected to predict the components in the reconfiguration of the monthly electricity consumption forecast. A forecasting program is developed based on R language and MATLAB, and a case study is conducted on the power consumption data of a university campus containing distributed energy. Results show that the proposed method is reasonable and effective.

scholarly journals Dynamic Demand Forecast and Assignment Model for Bike-and-Ride System

2019 ◽  
Vol 31 (6) ◽  
pp. 621-632
Author(s):  
Siyuan Zhang ◽  
Shijun Yu ◽  
Shejun Deng ◽  
Qinghui Nie ◽  
Pengpeng Zhang ◽  
...  
Keyword(s):  
Influencing Factors ◽  
Traffic Congestion ◽  
Forecast Model ◽  
Demand Forecast ◽  
Dynamic Demand ◽  
Forecasting Accuracy ◽  
Assignment Model ◽  
Forecasting Methods ◽  
Generalised Cost

Bike-and-Ride (B&R) has long been considered as an effective way to deal with urbanization-related issues such as traffic congestion, emissions, equality, etc. Although there are some studies focused on the B&R demand forecast, the influencing factors from previous studies have been excluded from those forecasting methods. To fill this gap, this paper proposes a new B&R demand forecast model considering the influencing factors as dynamic rather than fixed ones to reach higher forecasting accuracy. This model is tested in a theoretical network to validate the feasibility and effectiveness and the results show that the generalised cost does have an effect on the demand for the B&R system.

scholarly journals Forecasting Electricity Consumption Using Fuzzy Time Series

2018 ◽  
Vol 7 (4.30) ◽  
pp. 342
Author(s):  
K.G. Tay ◽  
Y.Y. Choy ◽  
C.C. Chew
Keyword(s):  
Time Series ◽  
Membership Function ◽  
Electricity Consumption ◽  
Fuzzy Time Series ◽  
Infrastructure Development ◽  
Power Load ◽  
Increasing Demand ◽  
Facility Expansion ◽  
Electric Power Load ◽  
Monthly Electricity Consumption

Electricity consumption forecasting is important for effective operation, planning and facility expansion of power system.  Accurate forecasts can save operating and maintenance costs, increased the reliability of power supply and delivery system, and correct decisions for future development.  There is a great development of Universiti Tun Hussein Onn Malaysia (UTHM) infrastructure since its formation in 1993. The development will be accompanied with the increasing demand of electricity.  Hence, there is a need to forecast the UTHM electricity consumption for future decisions on generating electric power, load switching, and infrastructure development. Therefore, in this study, the Fuzzy time series (FTS) with trapezoidal membership function was implemented on the UTHM monthly electricity consumption from January 2011 to December 2017 to forecast January to December 2018 monthly electricity consumption.  The procedure of the FTS and trapezoidal membership function was described together with January data.  FTS is able to forecast UTHM electricity consumption quite well.

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