PRONÓSTICO EFICIENTE DE LA DEMANDA DIARIA DEL SISTEMA ELECTRICO INTERCONECTADO DEL PERU MEDIANTE ANALISIS ESTOCASTICO ARIMA CON SUCESOS EXTERNOS

La demanda diaria del Sistema Eléctrico Interconectado Nacional-SEIN, posee características muy peculiares de tendencia, estacionalidad y aleatoriedad, situación que complica al proceso de estimación de su pronóstico. El objetivo del presente trabajo consiste en formular y calcular modelos ARIMA con Análisis de Sucesos Externos, a fin de lograr pronósticos eficientes de la demanda eléctrica de cada día siguiente, a nivel total y desagregado por áreas. Un buen pronóstico de la demanda diaria garantiza el despecho eficiente y económico de generación y transmisión, así como el aseguramiento y calidad de la demanda sectorial nacional. El enfoque metodológico lo constituye el tratamiento de cada serie temporal objetivo, mediante transformaciones estadístico-matemáticas apropiadas para alcanzar estabilidad tanto en varianzas como en medias regulares y estacionales; paralelamente filtrar los sucesos externos hasta alcanzar a un Modelo ARIMA predictivo de cada área del sistema eléctrico del Perú (Centro, Sur y Norte) y para cada día de la semana. Los resultados alcanzados en la presente investigación demuestran la eficiencia predictiva comparativa. Es decir, tomando como indicador de calidad de pronóstico al Error Absoluto Promedio Porcentual (MAPE), se han obtenido valores inferiores al 1% en las proyecciones de la demanda diaria total del SEIN, frente al 2% que se logra con actuales técnicas determinísticas. Palabras clave.- Pronóstico de Demanda, Despacho Eléctrico, ARIMA, Sucesos Externos, Serie Temporal, Proceso Estocástico, MAPE, Sistema Interconectado Nacional. ABSTRACTThe daily electric demand in Peruvian National Interconnected System-SEIN- has very particular trend, seasonality and characteristics external effects, a situation that complicates the process of estimating the short-term forecast. The aim of this paper is to formulate and calculate ARIMA models with External Events Analysis to achieve efficient forecasts of electricity demand each day, at total level and broken down by areas of the SEIN. The methodology is based on treating each time series using appropriate statistical-mathematical transformations to achieve stability in variance as regular seasonal averages, parallel external events to try to reach an optimal predictive model ARIMA each area of the electrical system of Peru (Central, South and North) and for each day of the week. The results demonstrate the predictive efficiency. Taking as a quality indicator forecast the Mean Absolute Percent Error (MAPE), have obtained values lower than 1% by the projections of the total daily demand SEIN versus 2% obtained with existing deterministic techniques. Keywords.- Demand Forecast, Electricity Demand, ARIMA, External Events, Time Series, Stochastic Process, MAPE, Electric Grid System.

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Electricity Demand Forecasting in Four Departments of Tahoua Region of Niger-Republic Using Time Series Model

International Journal of Innovative Research and Development ◽

10.24940/ijird/2017/v6/i10/jan17049 ◽

2017 ◽

Vol 6 (10) ◽

Author(s):

Yahaya Dan Gage Aboubacar ◽

A. Abdullahi Mati ◽

S. M. Sani

Keyword(s):

Time Series ◽

Demand Forecasting ◽

Electricity Demand ◽

Time Series Model ◽

Electricity Demand Forecasting

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Der Sommer und Herbst 2003 aus phänologischer Sicht | Summer and autumn 2003 from a phenological point of view

Schweizerische Zeitschrift fur Forstwesen ◽

10.3188/szf.2004.0142 ◽

2004 ◽

Vol 155 (5) ◽

pp. 142-145 ◽

Cited By ~ 2

Author(s):

Claudio Defila

Keyword(s):

Time Series ◽

New Record ◽

Point Of View ◽

Late Spring ◽

Mean Deviation ◽

Leaf Shedding ◽

The Mean ◽

Very High ◽

Selection Of

The record-breaking heatwave of 2003 also had an impact on the vegetation in Switzerland. To examine its influences seven phenological late spring and summer phases were evaluated together with six phases in the autumn from a selection of stations. 30% of the 122 chosen phenological time series in late spring and summer phases set a new record (earliest arrival). The proportion of very early arrivals is very high and the mean deviation from the norm is between 10 and 20 days. The situation was less extreme in autumn, where 20% of the 103 time series chosen set a new record. The majority of the phenological arrivals were found in the class «normal» but the class«very early» is still well represented. The mean precocity lies between five and twenty days. As far as the leaf shedding of the beech is concerned, there was even a slight delay of around six days. The evaluation serves to show that the heatwave of 2003 strongly influenced the phenological events of summer and spring.

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The influence of solar wind on extratropical cyclones – Part 1: Wilcox effect revisited

Annales Geophysicae ◽

10.5194/angeo-27-1-2009 ◽

2009 ◽

Vol 27 (1) ◽

pp. 1-30 ◽

Cited By ~ 24

Author(s):

P. Prikryl ◽

V. Rušin ◽

M. Rybanský

Keyword(s):

Time Series ◽

Solar Wind ◽

Northern Hemisphere ◽

High Speed ◽

Coronal Holes ◽

Extratropical Cyclones ◽

Sector Boundary ◽

Speed Solar Wind ◽

The Mean ◽

High Speed Solar Wind

Abstract. A sun-weather correlation, namely the link between solar magnetic sector boundary passage (SBP) by the Earth and upper-level tropospheric vorticity area index (VAI), that was found by Wilcox et al. (1974) and shown to be statistically significant by Hines and Halevy (1977) is revisited. A minimum in the VAI one day after SBP followed by an increase a few days later was observed. Using the ECMWF ERA-40 re-analysis dataset for the original period from 1963 to 1973 and extending it to 2002, we have verified what has become known as the "Wilcox effect" for the Northern as well as the Southern Hemisphere winters. The effect persists through years of high and low volcanic aerosol loading except for the Northern Hemisphere at 500 mb, when the VAI minimum is weak during the low aerosol years after 1973, particularly for sector boundaries associated with south-to-north reversals of the interplanetary magnetic field (IMF) BZ component. The "disappearance" of the Wilcox effect was found previously by Tinsley et al. (1994) who suggested that enhanced stratospheric volcanic aerosols and changes in air-earth current density are necessary conditions for the effect. The present results indicate that the Wilcox effect does not require high aerosol loading to be detected. The results are corroborated by a correlation with coronal holes where the fast solar wind originates. Ground-based measurements of the green coronal emission line (Fe XIV, 530.3 nm) are used in the superposed epoch analysis keyed by the times of sector boundary passage to show a one-to-one correspondence between the mean VAI variations and coronal holes. The VAI is modulated by high-speed solar wind streams with a delay of 1–2 days. The Fourier spectra of VAI time series show peaks at periods similar to those found in the solar corona and solar wind time series. In the modulation of VAI by solar wind the IMF BZ seems to control the phase of the Wilcox effect and the depth of the VAI minimum. The mean VAI response to SBP associated with the north-to-south reversal of BZ is leading by up to 2 days the mean VAI response to SBP associated with the south-to-north reversal of BZ. For the latter, less geoeffective events, the VAI minimum deepens (with the above exception of the Northern Hemisphere low-aerosol 500-mb VAI) and the VAI maximum is delayed. The phase shift between the mean VAI responses obtained for these two subsets of SBP events may explain the reduced amplitude of the overall Wilcox effect. In a companion paper, Prikryl et al. (2009) propose a new mechanism to explain the Wilcox effect, namely that solar-wind-generated auroral atmospheric gravity waves (AGWs) influence the growth of extratropical cyclones. It is also observed that severe extratropical storms, explosive cyclogenesis and significant sea level pressure deepenings of extratropical storms tend to occur within a few days of the arrival of high-speed solar wind. These observations are discussed in the context of the proposed AGW mechanism as well as the previously suggested atmospheric electrical current (AEC) model (Tinsley et al., 1994), which requires the presence of stratospheric aerosols for a significant (Wilcox) effect.

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Technical note: Inherent benchmark or not? Comparing Nash–Sutcliffe and Kling–Gupta efficiency scores

Hydrology and Earth System Sciences ◽

10.5194/hess-23-4323-2019 ◽

2019 ◽

Vol 23 (10) ◽

pp. 4323-4331 ◽

Cited By ~ 60

Author(s):

Wouter J. M. Knoben ◽

Jim E. Freer ◽

Ross A. Woods

Keyword(s):

Time Series ◽

Coefficient Of Variation ◽

Ad Hoc ◽

Model Performance ◽

Technical Note ◽

Mean Flow ◽

Model Adequacy ◽

Robust Model ◽

The Mean ◽

Adequacy Assessment

Abstract. A traditional metric used in hydrology to summarize model performance is the Nash–Sutcliffe efficiency (NSE). Increasingly an alternative metric, the Kling–Gupta efficiency (KGE), is used instead. When NSE is used, NSE = 0 corresponds to using the mean flow as a benchmark predictor. The same reasoning is applied in various studies that use KGE as a metric: negative KGE values are viewed as bad model performance, and only positive values are seen as good model performance. Here we show that using the mean flow as a predictor does not result in KGE = 0, but instead KGE =1-√2≈-0.41. Thus, KGE values greater than −0.41 indicate that a model improves upon the mean flow benchmark – even if the model's KGE value is negative. NSE and KGE values cannot be directly compared, because their relationship is non-unique and depends in part on the coefficient of variation of the observed time series. Therefore, modellers who use the KGE metric should not let their understanding of NSE values guide them in interpreting KGE values and instead develop new understanding based on the constitutive parts of the KGE metric and the explicit use of benchmark values to compare KGE scores against. More generally, a strong case can be made for moving away from ad hoc use of aggregated efficiency metrics and towards a framework based on purpose-dependent evaluation metrics and benchmarks that allows for more robust model adequacy assessment.

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Modeling of Lake Malombe Annual Fish Landings and Catch per Unit Effort (CPUE)

Forecasting ◽

10.3390/forecast3010004 ◽

2021 ◽

Vol 3 (1) ◽

pp. 39-55

Author(s):

Rodgers Makwinja ◽

Seyoum Mengistou ◽

Emmanuel Kaunda ◽

Tena Alemiew ◽

Titus Bandulo Phiri ◽

...

Keyword(s):

Time Series ◽

Information Criterion ◽

Coefficient Of Determination ◽

Series Data ◽

Efficiency Coefficient ◽

Arima Models ◽

Catch Per Unit Effort ◽

Annual Fish ◽

Unit Effort ◽

The Mean

Forecasting, using time series data, has become the most relevant and effective tool for fisheries stock assessment. Autoregressive integrated moving average (ARIMA) modeling has been commonly used to predict the general trend for fish landings with increased reliability and precision. In this paper, ARIMA models were applied to predict Lake Malombe annual fish landings and catch per unit effort (CPUE). The annual fish landings and CPUE trends were first observed and both were non-stationary. The first-order differencing was applied to transform the non-stationary data into stationary. Autocorrelation functions (AC), partial autocorrelation function (PAC), Akaike information criterion (AIC), Bayesian information criterion (BIC), square root of the mean square error (RMSE), the mean absolute error (MAE), percentage standard error of prediction (SEP), average relative variance (ARV), Gaussian maximum likelihood estimation (GMLE) algorithm, efficiency coefficient (E2), coefficient of determination (R2), and persistent index (PI) were estimated, which led to the identification and construction of ARIMA models, suitable in explaining the time series and forecasting. According to the measures of forecasting accuracy, the best forecasting models for fish landings and CPUE were ARIMA (0,1,1) and ARIMA (0,1,0). These models had the lowest values AIC, BIC, RMSE, MAE, SEP, ARV. The models further displayed the highest values of GMLE, PI, R2, and E2. The “auto. arima ()” command in R version 3.6.3 further displayed ARIMA (0,1,1) and ARIMA (0,1,0) as the best. The selected models satisfactorily forecasted the fish landings of 2725.243 metric tons and CPUE of 0.097 kg/h by 2024.

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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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The Initial Return and Its Conditional Return Volatility: Evidence from the Chinese IPO Market

Review of Pacific Basin Financial Markets and Policies ◽

10.1142/s0219091514500222 ◽

2014 ◽

Vol 17 (04) ◽

pp. 1450022 ◽

Cited By ~ 1

Author(s):

M. Monica Hussein ◽

Zhong-Guo Zhou

Keyword(s):

Time Series ◽

Linear Functions ◽

Return Volatility ◽

Cross Sectional ◽

Variance Model ◽

Initial Return ◽

Traditional Market ◽

Mean And Variance ◽

The Mean ◽

Sectional Analysis

This paper investigates the monthly initial return and its conditional return volatility for Chinese IPOs. We find that the mean initial return (IR) and cross-sectional return volatility are highly auto- and cross-correlated, and time-varying. We propose a system of two simultaneous equations: a GARCH-in-mean (GARCH-M) process with an ARMA(1,1) adjustment in the residuals for the IR and an EGARCH process for the conditional return volatility, assuming that the IR and its conditional return volatility are linear functions of the same market, firm- and offer-specific characteristics. We find that the model captures both time-series and cross-sectional correlations at the mean and variance levels. Our findings suggest that the conditional return volatility affects the IR positively and significantly, in addition to the traditional market, firm- and offer-specific characteristics. IPOs with higher conditional return volatility, as a proxy for information asymmetry, tend to be underpriced more. The paper demonstrates the merit of using a conditional variance model, along with time series and cross-sectional analysis to price Chinese IPOs.

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