Short-term load forecasting method based on fuzzy time series, seasonality and long memory process

Sadaei, Hossein Javedani; Guimares, Frederico Gadelha; Silva, Cidiney Jos da; Lee, Muhammad Hisyam; Eslami, Tayyebeh

doi:10.1016/j.ijar.2017.01.006

Cited by 106 publications

(42 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Artificial intelligence techniques are used in the prediction system. In load forecasting, the prediction is typically divided into short term load forecasting (STLF), medium term load forecasting (MTLF), and long term load forecasting (LTLF) [21][22][23]. LTLF is widely used today to decide when it is necessary to upgrade existing electricity distribution systems and build new lines or substations.…”

Section: Forewordmentioning

confidence: 99%

The Fuzzy Logic Method to Efficiently Optimize Electricity Consumption in Individual Housing

2017

View full text Add to dashboard Cite

Electricity demand shifting and reduction still raise a huge interest for end-users at the household level, especially because of the ongoing design of a dynamic pricing approach. In particular, end-users must act as the starting point for decreasing their consumption during peak hours to prevent the need to extend the grid and thus save considerable costs. This article points out the relevance of a fuzzy logic algorithm to efficiently predict short term load consumption (STLC). This approach is the cornerstone of a new home energy management (HEM) algorithm which is able to optimize the cost of electricity consumption, while smoothing the peak demand. The fuzzy logic modeling involves a strong reliance on a complete database of real consumption data from many instrumented show houses. The proposed HEM algorithm enables any end-user to manage his electricity consumption with a high degree of flexibility and transparency, and "reshape" the load profile. For example, this can be mainly achieved using smart control of a storage system coupled with remote management of the electric appliances. The simulation results demonstrate that an accurate prediction of STLC gives the possibility of achieving optimal planning and operation of the HEM system.

show abstract

Section: Forewordmentioning

confidence: 99%

The Fuzzy Logic Method to Efficiently Optimize Electricity Consumption in Individual Housing

2017

View full text Add to dashboard Cite

show abstract

“…Many experts and scholars have done a lot of research on prediction theory and methods and put forward several prediction models and methods [7][8][9][10][11]. At present, the prediction method of power load can be divided into two categories [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Short-Term Power Load Forecasting Method Based on Improved Exponential Smoothing Grey Model

Fan

Duan

et al. 2018

Mathematical Problems in Engineering

View full text Add to dashboard Cite

In order to improve the prediction accuracy, this paper proposes a short-term power load forecasting method based on the improved exponential smoothing grey model. It firstly determines the main factor affecting the power load using the grey correlation analysis. It then conducts power load forecasting using the improved multivariable grey model. The improved prediction model firstly carries out the smoothing processing of the original power load data using the first exponential smoothing method. Secondly, the grey prediction model with an optimized background value is established using the smoothed sequence which agrees with the exponential trend. Finally, the inverse exponential smoothing method is employed to restore the predicted value. The first exponential smoothing model uses the 0.618 method to search for the optimal smooth coefficient. The prediction model can take the effects of the influencing factors on the power load into consideration. The simulated results show that the proposed prediction algorithm has a satisfactory prediction effect and meets the requirements of short-term power load forecasting. This research not only further improves the accuracy and reliability of short-term power load forecasting but also extends the application scope of the grey prediction model and shortens the search interval.

show abstract

“…Efendi et al [17] used a fuzzy time series model to forecast daily electricity load demand. Sadaei et al [18] proposed a short-term load forecasting model based on the seasonality memory process and fuzzy time series model. These fuzzy time series models are all autoregressive (AR) models.…”

Section: Introductionmentioning

confidence: 99%

A Two-Factor Autoregressive Moving Average Model Based on Fuzzy Fluctuation Logical Relationships

Guan

Zhao

2017

Symmetry

View full text Add to dashboard Cite

Many of the existing autoregressive moving average (ARMA) forecast models are based on one main factor. In this paper, we proposed a new two-factor first-order ARMA forecast model based on fuzzy fluctuation logical relationships of both a main factor and a secondary factor of a historical training time series. Firstly, we generated a fluctuation time series (FTS) for two factors by calculating the difference of each data point with its previous day, then finding the absolute means of the two FTSs. We then constructed a fuzzy fluctuation time series (FFTS) according to the defined linguistic sets. The next step was establishing fuzzy fluctuation logical relation groups (FFLRGs) for a two-factor first-order autoregressive (AR (1)) model and forecasting the training data with the AR(1) model. Then we built FFLRGs for a two-factor first-order autoregressive moving average (ARMA(1,m)) model. Lastly, we forecasted test data with the ARMA(1,m) model. To illustrate the performance of our model, we used real Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and Dow Jones datasets as a secondary factor to forecast TAIEX. The experiment results indicate that the proposed two-factor fluctuation ARMA method outperformed the one-factor method based on real historic data. The secondary factor may have some effects on the main factor and thereby impact the forecasting results. Using fuzzified fluctuations rather than fuzzified real data could avoid the influence of extreme values in historic data, which performs negatively while forecasting. To verify the accuracy and effectiveness of the model, we also employed our method to forecast the Shanghai Stock Exchange Composite Index (SHSECI) from 2001 to 2015 and the international gold price from 2000 to 2010.

show abstract

Short-term load forecasting method based on fuzzy time series, seasonality and long memory process

Cited by 106 publications

References 30 publications

The Fuzzy Logic Method to Efficiently Optimize Electricity Consumption in Individual Housing

The Fuzzy Logic Method to Efficiently Optimize Electricity Consumption in Individual Housing

Short-Term Power Load Forecasting Method Based on Improved Exponential Smoothing Grey Model

A Two-Factor Autoregressive Moving Average Model Based on Fuzzy Fluctuation Logical Relationships

Contact Info

Product

Resources

About