Energy Time Series Forecasting

Dannecker, Lars

doi:10.1007/978-3-658-11039-0

Cited by 24 publications

(18 citation statements)

References 95 publications

(233 reference statements)

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“…For long-term analysis, soft computing techniques are frequently preferred (Coimbra et al, 2013;Demirhan & Renwick, 2018). The autoregressive models describe the characteristics and behavior of the time series using an autoregression process (Antonanzas et al, 2016;Dannecker, 2015). ARIMA is an extension of the ARMA model, which models non-stationary time series.…”

Section: Discussionmentioning

confidence: 99%

“…AR describes the past behavior of the time series and series residual at the actual time as a weighted linear combination of values of a dataset of a stochastic process x t (Dannecker, 2015) and a white noise u t as follows…”

Section: Arimamentioning

confidence: 99%

“…Solar irradiance forecasting allows estimating the electricity generation in the long-mediumand short-term. This information is crucial to maintain the balance between the electricity demand and 1 Introduction supply (Dannecker, 2015) and minimizing costs associated with the start and shutdown of conventional power plants (Badosa et al, 2017). These models facilitate the implementation of PV systems, both ongrid and off-grid.…”

Section: Introductionmentioning

confidence: 99%

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Sustainable energy planning decision using the intuitionistic fuzzy analytic hierarchy process: choosing energy technology in Malaysia

Abdullah

Najib

2014

International Journal of Sustainable Energy

112

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Article [I]Introducing the community to technical projects requires a deal with the social, energy and environmental policies as well as the cultural field. To address an energy project from a socio-technical view requires the joint analysis of both the project and the community. This work focuses on the formulation of a methodology to ease the prioritization of projects and community participation. To evaluate the community, the Human Development Index and Sustainable Development Goal Index are adjusted to the context and available information of Nariño. The Net Present Value is used for the project evaluation. The Analytic Hierarchy Process allows for the evaluation of the community and project jointly and establishing prioritization objectives. Moreover, the co-construction methodology is the basis to formulate guidelines to work with the community. This research found that there is a relationship between the projects that seek to improve the quality of the life and education in Nariño. Article [II]Solar irradiance is a worldwide available resource that could drive electrification processes in regions with low socio-economic indexes. Therefore, to know solar irradiance behavior and data is increasingly a mandatory activity. However, some interesting sites, generally socio-economic outcast places, do not rely on solar irradiance data, and if information exists, it is not complete. Therefore, researchers use some techniques to estimate this energy resource with information from other meteorological variables as temperature. Nevertheless, there is not a broad analysis of these techniques in tropical and mountainous environments. Therefore, this research analyzes the performance of three well-known empirical temperature-based models in tropical and mountainous environments. Moreover, this work proposes a new empirical technique that models solar irradiance in some areas better than the three techniques mentioned. Statistical error comparison allows us to choose the best model for each location and the data imputation model. Hargreaves and Samani's model presented better results in the Pacific zone, and the proposed model showed better results in the Andean and Amazon zones. Another significant result is the linear relationship between the new empirical model constants and the altitude 2.500 MASL. Article [III]The solar energy potential maps are an enabler for solar energy use. However, the lack of solar irradiance information is a barrier to elaborating on this type of decision tool. This research proposed the estimation of solar irradiance using air temperature data to increase the sampled points with the Hargreaves and Samani and a proposed empirical model. Also, the leave-one-out cross-validation is the technique used to assess the performance of four spatial interpolation techniques in a tropical and mountainous environment. The information came from Nariño state in Colombian that covers an area of 33.268km 2 . The proposed empirical model shows better performance in sites with an altitude above 2.500 MASL, lo...

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Section: Discussionmentioning

confidence: 99%

Section: Arimamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Sustainable energy planning decision using the intuitionistic fuzzy analytic hierarchy process: choosing energy technology in Malaysia

Abdullah

Najib

2014

International Journal of Sustainable Energy

112

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“…For example, the dynamic series data sets are unique cases, they usually contain several observable variables that exhibit long-range dependence or multifractal nature. Previous studies have demonstrated that turbulent flows, velocities, temperatures, stock markets, and concentration fields are embedded in the similar space as joint multifractal measures [1][2][3][4][5]. Reviewed previous literature, we found the autocorrelation and cross correlation functions (CCF) are useful for analyzing the joint behaviors of two stationary series whose behaviors may be related in some unspecified ways [6].…”

Section: Introductionmentioning

confidence: 99%

Predicting the Participation in Social Science under Expanding System by Using ARIMAX

Chang¹,

Chen²

2019

Proceedings of the 2019 2nd International Conference on Mathematics, Modeling and Simulation Technologies and Applications (MMS

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This study aims to predict the participation pattern related to the social science programs within a high participated higher education system. Taking the expanding higher education system in Taiwan as an example, we collected the series data with student numbers in social science programs and total student enrollment numbers by using the annual statistics report (1950 to 2017) from Ministry of Education. Considered the concurrent series did not fit the classical ARIMA (autoregressive integrated moving average) model, this study selected transfer function in terms of multivariate autoregressive integrated moving average (ARIMAX) models to deal with the target series. First, we applied the cross correlation function to check the relationships between the series. Second, we select the ARIMAX with transfer function to verify the fittest predicting model. The result reveals the selected ARIMAX(1,1,1) model works well for predicting the trend of social science participation in future. This study provides an example to tackle two series variables in ARIMAX process in higher education settings. The finding suggests useful information for related policy makers to renovating the social science programs.

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“…Another methodology used is the Box-Tiao model, which is called ARIMAX [14]; it represents an expansion of the ARIMA models that adds a linear component as a function of the covariate observations (or exogenous variables). The difference between the two is that ARIMAX has an exogenous input, in addition to the auto-regressive and moving averages parameters [15].…”

Section: Box-jenkins and Box-tiao Modelingmentioning

confidence: 99%

Innovative Hybrid Modeling of Wind Speed Prediction Involving Time-Series Models and Artificial Neural Networks

et al. 2018

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This work proposes hybrid models combining time-series models (using linear functions) and artificial intelligence (using a nonlinear function) that can be used to provide monthly mean wind speed predictions for the Brazilian northeast region. These might be useful for wind power generation; for example, they could acquire important information on how the local wind potential can be usable for a possible wind power plant through understanding future wind speed values. To create the proposed hybrid models, it was necessary to set the wind speed variable as a dependent variable of exogenous variables (i.e., pressure, temperature, and precipitation). Thus, it was possible to consider the meteorological characteristics of the study regions. It is possible to verify the hybrid models' efficiency in providing perfect adjustments to the observed data. This statement is based on the low values found in the error statistical analysis, i.e., an error of approximately 5.0% and a Nash-Sutcliffe coefficient near to 0.96. These results were certainly important in predicting the wind speed time-series, which was similar to the observed wind speed time-series profile. Great similarities of maximums and minimums between the series were evident and showed the capacity of the models to represent the seasonality characteristics.

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Energy Time Series Forecasting

Cited by 24 publications

References 95 publications

Sustainable energy planning decision using the intuitionistic fuzzy analytic hierarchy process: choosing energy technology in Malaysia

Sustainable energy planning decision using the intuitionistic fuzzy analytic hierarchy process: choosing energy technology in Malaysia

Predicting the Participation in Social Science under Expanding System by Using ARIMAX

Innovative Hybrid Modeling of Wind Speed Prediction Involving Time-Series Models and Artificial Neural Networks

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