Research on Combined Model Based on Multi-Objective Optimization and Application in Wind Speed Forecast

Wind power is an important part of a power system, and its use has been rapidly increasing as compared with fossil energy. However, due to the intermittence and randomness of wind speed, system operators and researchers urgently need to find more reliable wind-speed prediction methods. It was found that the time series of wind speed not only has linear characteristics, but also nonlinear. In addition, most methods only consider one criterion or rule (stability or accuracy), or one objective function, which can lead to poor forecasting results. So, wind-speed forecasting is still a difficult and challenging problem. The existing forecasting models based on combination-model theory can adapt to some time-series data and overcome the shortcomings of the single model, which achieves poor accuracy and instability. In this paper, a combined forecasting model based on data preprocessing, a nondominated sorting genetic algorithm (NSGA-III) with three objective functions and four models (two hybrid nonlinear models and two linear models) is proposed and was successfully applied to forecasting wind speed, which not only overcomes the issue of forecasting accuracy, but also solves the difficulties of forecasting stability. The experimental results show that the stability and accuracy of the proposed combined model are better than the single models, improving the mean absolute percentage error (MAPE) range from 0.007% to 2.31%, and the standard deviation mean absolute percentage error (STDMAPE) range from 0.0044 to 0.3497.

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Prediksi Jumlah Mahasiswa Ujian Skripsi Dengan Metode Least Square

SITECH Jurnal Sistem Informasi dan Teknologi ◽

10.24176/sitech.v4i1.6224 ◽

2021 ◽

Vol 4 (1) ◽

pp. 43-50

Author(s):

Amiruddin Bengnga ◽

Rezqiwati Ishak

Keyword(s):

Time Series ◽

Time Series Data ◽

Mean Absolute Percentage Error ◽

Least Square ◽

Series Data ◽

Percentage Error ◽

Absolute Percentage Error

Pencapaian target jumlah mahasiswa yang ujian skripsi di setiap semester pada setiap Program Studi yang sudah ditentukan di awal semester tentunya ada yang mencapai target dan tidak mencapai target. Jika tidak mencapai target maka hal ini akan menjadi penilaian kinerja Program Studi menurun, agar hal ini tidak terjadi, maka salah satu soluisnya adalah melakukan teknik prediksi dengan menggunakan data yang relevan di periode semester sebelumnya. Metode prediksi yang digunakan adalah metode Least Square karena metode ini cocok digunakan untuk memprediksi data dalam bentuk time series. Data yang digunakan dalam penelitian ini diambil dari 5 Program Studi di Universitas Ichsan Gorontalo. Berdasarkan hasil penelitian perhitungan tingkat kesalahan dengan menggunakan MAPE (Mean Absolute Percentage Error) antara data aktual dan hasil prediksi didapatkan rata-rata error sebesar 16.11% atau tingkat akurasi sebesar 83.89%.

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Peramalan Data Kunjungan Wisatawan Mancanegara ke Indonesia menggunakan Fuzzy Time Series

Jurnal Edukasi dan Penelitian Informatika (JEPIN) ◽

10.26418/jp.v5i1.31074 ◽

2019 ◽

Vol 5 (1) ◽

pp. 18

Author(s):

Indra Jiwana Thira ◽

Nissa Almira Mayangky ◽

Desiana Nur Kholifah ◽

Imanuel Balla ◽

Windu Gata

Keyword(s):

Time Series ◽

Time Series Data ◽

Mean Absolute Percentage Error ◽

Fuzzy Time Series ◽

Series Data ◽

Percentage Error ◽

Absolute Percentage Error

Wisatawan mancanegara memegang peranan penting terhadap pertumbuhan ekonomi dari sektor pariwisata. Untuk meningkatkan kunjungan wisatawan mancanegara perlu dilakukan pembangunan yang berkelanjutan pada sektor pariwisata. Pembangunan yang dilakukan harus sejalan dengan tren pertumbuhan kunjungan wisatawan mancanegara agar pembangunan tepat sasaran, efektif dan efisien. penelitian ini bertujuan untuk meramalkan kunjungan wisatawan mancanegara ke Indonesia menggunakan metode Fuzzy Time Series. Data historis yang digunakan adalah data kunjungan wisatawan mancanegara ke Indonesia periode Januari Tahun 2013 sampai dengan Desember Tahun 2017 dari Badan Pusat Statistik (BPS). Implementasi Fuzzy Time Series pada data historis menghasilkan Mean Absolute Percentage Error (MAPE) sebesar 4,42 % dengan tingkat kesalahan tertinggi sebesar sebesar 18,05% pada Januari 2014 dan kesalahan terendah sebesar 0,04% pada Mei 2017. Hasil tersebut menunjukan bahwa penggunakan Fuzzy Time Series pada peramalan data kunjungan wisatawan mancanegara ke Indonesia memiliki hasil yang sangat baik.

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SRPM–CNN: a combined model based on slide relative position matrix and CNN for time series classification

Complex & Intelligent Systems ◽

10.1007/s40747-021-00296-y ◽

2021 ◽

Author(s):

Taoying Li ◽

Yuqi Zhang ◽

Ting Wang

Keyword(s):

Time Series ◽

Relative Position ◽

Time Series Data ◽

Series Data ◽

Time Series Classification ◽

Combined Model ◽

Daily Work ◽

Mining Areas ◽

Model Based ◽

Proposed Model

AbstractResearch on the time series classification is gaining an increased attention in the machine learning and data mining areas due to the existence of the time series data almost everywhere, especially in our daily work and life. Recent studies have shown that the convolutional neural networks (CNN) can extract good features from the images and texts, but it often encounters the problem of low accuracy, when it is directly employed to solve the problem of time series classification. In this pursuit, the present study envisaged a novel combined model based on the slide relative position matrix and CNN for time series. The proposed model first adopted the slide relative position for converting the time series data into 2D images during preprocessing, and then employed CNN to classify these images. This made the best of the temporal sequence characteristic of time series data, thereby utilizing the advantages of CNN in image recognition. Finally, 14 UCR time series datasets were chosen to evaluate the performance of the proposed model, whose results indicate that the accuracy of the proposed model was higher than others.

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Univariate Time Series Prediction of Wind speed with a case study of Yanbu, Saudi Arabia

International Journal of Advanced Trends in Computer Science and Engineering ◽

10.30534/ijatcse/2021/361012021 ◽

2021 ◽

Vol 10 (1) ◽

pp. 257-264

Keyword(s):

Time Series ◽

Saudi Arabia ◽

Wind Speed ◽

Wind Energy ◽

Mean Absolute Percentage Error ◽

Percentage Error ◽

Wind Speed Data ◽

Absolute Percentage Error ◽

Univariate Time Series

Wind energy is a promising alternativefor renewable source of energy pursued world-wide to reduce carbon emissions for a green future. The prediction of wind speed is a challenging subject and plays an instrumental role in development of wind power systems (particularly grid connected renewable energy systems where predicting wind speed facilitates manipulation of the load on the grid). Modern machine learning techniques including neural networks have been widely utilized for this purpose. Literature indicates availability of several models for estimation of the wind speed one hour ahead and the hourly wind speed data profile one day ahead. This paper considers the prediction of wind energy as a univariate time series (UVT) prediction problem and employs major prediction algorithms including the K-Nearest Neighbors (kNN), Random Forest (RF), Support Vector Regression (SVR), Holt-Winter and ARIMA method. Forecasting a univariate time series depends only on past wind speed data values, rather than use of external data attributes like wind direction or weather forecast for prediction algorithm. In the present study (as a case-study), 13 years of hourly average wind speed data (of the period 1970-1982) of Yanbu, Saudi Arabia has been utilized to evaluate the performance of selected algorithms. Yanbu is an industrial city that plays a major role in the economy of Saudi Arabia. The findings showed that SVR, RF and ARIMA methods exhibit a better forecastingperformance in relation to four evaluation parameters of Mean Absolute Percentage Error(MAPE),Symmetric Mean Absolute Percentage Error (sMAPE),Mean Absolute Error (MAE) and Mean Absolute Scaled Error (MASE).

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The Use of Odd and Even Class Wind Speed Time Series of Distribution Histogram to Estimate Weibull Parameters

International Journal of Renewable Energy Development ◽

10.14710/ijred.7.2.139-150 ◽

2018 ◽

Vol 7 (2) ◽

pp. 139-150 ◽

Cited By ~ 1

Author(s):

Adekunlé Akim Salami ◽

Ayité Sénah Akoda Ajavon ◽

Mawugno Koffi Kodjo ◽

Seydou Ouedraogo ◽

Koffi-Sa Bédja

Keyword(s):

Time Series ◽

Wind Speed ◽

Time Series Data ◽

Empirical Method ◽

Likelihood Method ◽

Series Data ◽

New Approach ◽

Weibull Parameters ◽

Distribution Histogram ◽

Mean Wind Speed

In this article, we introduced a new approach based on graphical method (GPM), maximum likelihood method (MLM), energy pattern factor method (EPFM), empirical method of Justus (EMJ), empirical method of Lysen (EML) and moment method (MOM) using the even or odd classes of wind speed series distribution histogram with 1 m/s as bin size to estimate the Weibull parameters. This new approach is compared on the basis of the resulting mean wind speed and its standard deviation using seven reliable statistical indicators (RPE, RMSE, MAPE, MABE, R2, RRMSE and IA). The results indicate that this new approach is adequate to estimate Weibull parameters and can outperform GPM, MLM, EPF, EMJ, EML and MOM which uses all wind speed time series data collected for one period. The study has also found a linear relationship between the Weibull parameters K and C estimated by MLM, EPFM, EMJ, EML and MOM using odd or even class wind speed time series and those obtained by applying these methods to all class (both even and odd bins) wind speed time series. Another interesting feature of this approach is the data size reduction which eventually leads to a reduced processing time.Article History: Received February 16th 2018; Received in revised form May 5th 2018; Accepted May 27th 2018; Available onlineHow to Cite This Article: Salami, A.A., Ajavon, A.S.A., Kodjo, M.K. , Ouedraogo, S. and Bédja, K. (2018) The Use of Odd and Even Class Wind Speed Time Series of Distribution Histogram to Estimate Weibull Parameters. Int. Journal of Renewable Energy Development 7(2), 139-150.https://doi.org/10.14710/ijred.7.2.139-150

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Load forecasting of refrigerated display cabinet based on CEEMD–IPSO–LSTM combined model

Open Physics ◽

10.1515/phys-2021-0043 ◽

2021 ◽

Vol 19 (1) ◽

pp. 360-374

Author(s):

Yuan Pei ◽

Lei Zhenglin ◽

Zeng Qinghui ◽

Wu Yixiao ◽

Lu Yanli ◽

...

Keyword(s):

Time Series ◽

Deep Learning ◽

Time Series Data ◽

Load Forecasting ◽

Series Data ◽

Forecasting Model ◽

Combined Model ◽

Forecasting Accuracy ◽

Forecasting Method ◽

Consumption Reduction

Abstract The load of the showcase is a nonlinear and unstable time series data, and the traditional forecasting method is not applicable. Deep learning algorithms are introduced to predict the load of the showcase. Based on the CEEMD–IPSO–LSTM combination algorithm, this paper builds a refrigerated display cabinet load forecasting model. Compared with the forecast results of other models, it finally proves that the CEEMD–IPSO–LSTM model has the highest load forecasting accuracy, and the model’s determination coefficient is 0.9105, which is obviously excellent. Compared with other models, the model constructed in this paper can predict the load of showcases, which can provide a reference for energy saving and consumption reduction of display cabinet.

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Distance variable improvement of time-series big data stream evaluation

Journal Of Big Data ◽

10.1186/s40537-020-00359-w ◽

2020 ◽

Vol 7 (1) ◽

Author(s):

Ari Wibisono ◽

Petrus Mursanto ◽

Jihan Adibah ◽

Wendy D. W. T. Bayu ◽

May Iffah Rizki ◽

...

Keyword(s):

Time Series ◽

Standard Deviation ◽

Standard Method ◽

Time Series Data ◽

Series Data ◽

Percentage Error ◽

Traffic Demand ◽

Incremental Model ◽

Chernoff Bound ◽

The Mean

Abstract Real-time information mining of a big dataset consisting of time series data is a very challenging task. For this purpose, we propose using the mean distance and the standard deviation to enhance the accuracy of the existing fast incremental model tree with the drift detection (FIMT-DD) algorithm. The standard FIMT-DD algorithm uses the Hoeffding bound as its splitting criterion. We propose the further use of the mean distance and standard deviation, which are used to split a tree more accurately than the standard method. We verify our proposed method using the large Traffic Demand Dataset, which consists of 4,000,000 instances; Tennet’s big wind power plant dataset, which consists of 435,268 instances; and a road weather dataset, which consists of 30,000,000 instances. The results show that our proposed FIMT-DD algorithm improves the accuracy compared to the standard method and Chernoff bound approach. The measured errors demonstrate that our approach results in a lower Mean Absolute Percentage Error (MAPE) in every stage of learning by approximately 2.49% compared with the Chernoff Bound method and 19.65% compared with the standard method.

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Towards optimal ATM cash replenishment using time series analysis

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-201953 ◽

2021 ◽

pp. 1-13

Author(s):

Muhammad Rafi ◽

Mohammad Taha Wahab ◽

Muhammad Bilal Khan ◽

Hani Raza

Keyword(s):

Time Series ◽

Moving Average ◽

Arima Model ◽

Mean Absolute Percentage Error ◽

Percentage Error ◽

Absolute Percentage Error ◽

Automatic Teller Machine ◽

Proposed Model ◽

Auto Regressive ◽

Specific Amount

Automatic Teller Machine (ATM) are still largely used to dispense cash to the customers. ATM cash replenishment is a process of refilling ATM machine with a specific amount of cash. Due to vacillating users demands and seasonal patterns, it is a very challenging problem for the financial institutions to keep the optimal amount of cash for each ATM. In this paper, we present a time series model based on Auto Regressive Integrated Moving Average (ARIMA) technique called Time Series ARIMA Model for ATM (TASM4ATM). This study used ATM back-end refilling historical data from 6 different financial organizations in Pakistan. There are 2040 distinct ATMs and 18 month of replenishment data from these ATMs are used to train the proposed model. The model is compared with the state-of- the-art models like Recurrent Neural Network (RNN) and Amazon’s DeepAR model. Two approaches are used for forecasting (i) Single ATM and (ii) clusters of ATMs (In which ATMs are clustered with similar cash-demands). The Mean Absolute Percentage Error (MAPE) and Symmetric Mean Absolute Percentage Error (SMAPE) are used to evaluate the models. The suggested model produces far better forecasting as compared to the models in comparison and produced an average of 7.86/7.99 values for MAPE/SMAPE errors on individual ATMs and average of 6.57/6.64 values for MAPE/SMAPE errors on clusters of ATMs.

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An improved framework to predict river flow time series data

PeerJ ◽

10.7717/peerj.7183 ◽

2019 ◽

Vol 7 ◽

pp. e7183 ◽

Cited By ~ 1

Author(s):

Hafiza Mamona Nazir ◽

Ijaz Hussain ◽

Ishfaq Ahmad ◽

Muhammad Faisal ◽

Ibrahim M. Almanjahie

Keyword(s):

Time Series ◽

Water Resource Management ◽

River Flow ◽

Time Series Data ◽

Flow Time ◽

Indus Basin ◽

Series Data ◽

Percentage Error ◽

Empirical Bayesian ◽

Noise Characteristics

Due to non-stationary and noise characteristics of river flow time series data, some pre-processing methods are adopted to address the multi-scale and noise complexity. In this paper, we proposed an improved framework comprising Complete Ensemble Empirical Mode Decomposition with Adaptive Noise-Empirical Bayesian Threshold (CEEMDAN-EBT). The CEEMDAN-EBT is employed to decompose non-stationary river flow time series data into Intrinsic Mode Functions (IMFs). The derived IMFs are divided into two parts; noise-dominant IMFs and noise-free IMFs. Firstly, the noise-dominant IMFs are denoised using empirical Bayesian threshold to integrate the noises and sparsities of IMFs. Secondly, the denoised IMF’s and noise free IMF’s are further used as inputs in data-driven and simple stochastic models respectively to predict the river flow time series data. Finally, the predicted IMF’s are aggregated to get the final prediction. The proposed framework is illustrated by using four rivers of the Indus Basin System. The prediction performance is compared with Mean Square Error, Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). Our proposed method, CEEMDAN-EBT-MM, produced the smallest MAPE for all four case studies as compared with other methods. This suggests that our proposed hybrid model can be used as an efficient tool for providing the reliable prediction of non-stationary and noisy time series data to policymakers such as for planning power generation and water resource management.

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