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Forecasting, Volume 3, Issue 4 (December 2021) – 16 articles

Cover Story (view full-size image): In this study, the Holt method is modified using time-varying smoothing parameters instead of fixed on time. Smoothing parameters are obtained for each observation from first-order autoregressive models. The parameters of the autoregressive models are estimated using a harmony search algorithm, and forecasts are obtained with a subsampling bootstrap approach. The main contribution of the paper is to consider time-varying smoothing parameters with autoregressive equations and use the bootstrap method in an exponential smoothing method. Real-world time series are used to show the forecasting performance of the proposed method. View this paper
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20 pages, 1719 KiB  
Article
A Deep Learning Model for Forecasting Velocity Structures of the Loop Current System in the Gulf of Mexico
by Ali Muhamed Ali, Hanqi Zhuang, James VanZwieten, Ali K. Ibrahim and Laurent Chérubin
Forecasting 2021, 3(4), 934-953; https://doi.org/10.3390/forecast3040056 - 14 Dec 2021
Cited by 5 | Viewed by 2857
Abstract
Despite the large efforts made by the ocean modeling community, such as the GODAE (Global Ocean Data Assimilation Experiment), which started in 1997 and was renamed as OceanPredict in 2019, the prediction of ocean currents has remained a challenge until the present day—particularly [...] Read more.
Despite the large efforts made by the ocean modeling community, such as the GODAE (Global Ocean Data Assimilation Experiment), which started in 1997 and was renamed as OceanPredict in 2019, the prediction of ocean currents has remained a challenge until the present day—particularly in ocean regions that are characterized by rapid changes in their circulation due to changes in atmospheric forcing or due to the release of available potential energy through the development of instabilities. Ocean numerical models’ useful forecast window is no longer than two days over a given area with the best initialization possible. Predictions quickly diverge from the observational field throughout the water and become unreliable, despite the fact that they can simulate the observed dynamics through other variables such as temperature, salinity and sea surface height. Numerical methods such as harmonic analysis are used to predict both short- and long-term tidal currents with significant accuracy. However, they are limited to the areas where the tide was measured. In this study, a new approach to ocean current prediction based on deep learning is proposed. This method is evaluated on the measured energetic currents of the Gulf of Mexico circulation dominated by the Loop Current (LC) at multiple spatial and temporal scales. The approach taken herein consists of dividing the velocity tensor into planes perpendicular to each of the three Cartesian coordinate system directions. A Long Short-Term Memory Recurrent Neural Network, which is best suited to handling long-term dependencies in the data, was thus used to predict the evolution of the velocity field in each plane, along each of the three directions. The predicted tensors, made of the planes perpendicular to each Cartesian direction, revealed that the model’s prediction skills were best for the flow field in the planes perpendicular to the direction of prediction. Furthermore, the fusion of all three predicted tensors significantly increased the overall skills of the flow prediction over the individual model’s predictions. The useful forecast period of this new model was greater than 4 days with a root mean square error less than 0.05 cm·s1 and a correlation coefficient of 0.6. Full article
(This article belongs to the Special Issue Feature Papers of Forecasting 2021)
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14 pages, 422 KiB  
Article
Model-Free Time-Aggregated Predictions for Econometric Datasets
by Kejin Wu and Sayar Karmakar
Forecasting 2021, 3(4), 920-933; https://doi.org/10.3390/forecast3040055 - 8 Dec 2021
Cited by 4 | Viewed by 2249
Abstract
Forecasting volatility from econometric datasets is a crucial task in finance. To acquire meaningful volatility predictions, various methods were built upon GARCH-type models, but these classical techniques suffer from instability of short and volatile data. Recently, a novel existing normalizing and variance-stabilizing (NoVaS) [...] Read more.
Forecasting volatility from econometric datasets is a crucial task in finance. To acquire meaningful volatility predictions, various methods were built upon GARCH-type models, but these classical techniques suffer from instability of short and volatile data. Recently, a novel existing normalizing and variance-stabilizing (NoVaS) method for predicting squared log-returns of financial data was proposed. This model-free method has been shown to possess more accurate and stable prediction performance than GARCH-type methods. However, whether this method can sustain this high performance for long-term prediction is still in doubt. In this article, we firstly explore the robustness of the existing NoVaS method for long-term time-aggregated predictions. Then, we develop a more parsimonious variant of the existing method. With systematic justification and extensive data analysis, our new method shows better performance than current NoVaS and standard GARCH(1,1) methods on both short- and long-term time-aggregated predictions. The success of our new method is remarkable since efficient predictions with short and volatile data always carry great importance. Additionally, this article opens potential avenues where one can design a model-free prediction structure to meet specific needs. Full article
(This article belongs to the Special Issue Feature Papers of Forecasting 2021)
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36 pages, 22248 KiB  
Article
Improving Hotel Room Demand Forecasts for Vienna across Hotel Classes and Forecast Horizons: Single Models and Combination Techniques Based on Encompassing Tests
by Ulrich Gunter
Forecasting 2021, 3(4), 884-919; https://doi.org/10.3390/forecast3040054 - 27 Nov 2021
Cited by 7 | Viewed by 3432
Abstract
The present study employs daily data made available by the STR SHARE Center covering the period from 1 January 2010 to 31 January 2020 for six Viennese hotel classes and their total. The forecast variable of interest is hotel room demand. As forecast [...] Read more.
The present study employs daily data made available by the STR SHARE Center covering the period from 1 January 2010 to 31 January 2020 for six Viennese hotel classes and their total. The forecast variable of interest is hotel room demand. As forecast models, (1) Seasonal Naïve, (2) Error Trend Seasonal (ETS), (3) Seasonal Autoregressive Integrated Moving Average (SARIMA), (4) Trigonometric Seasonality, Box–Cox Transformation, ARMA Errors, Trend and Seasonal Components (TBATS), (5) Seasonal Neural Network Autoregression (Seasonal NNAR), and (6) Seasonal NNAR with an external regressor (seasonal naïve forecast of the inflation-adjusted ADR) are employed. Forecast evaluation is carried out for forecast horizons h = 1, 7, 30, and 90 days ahead based on rolling windows. After conducting forecast encompassing tests, (a) mean, (b) median, (c) regression-based weights, (d) Bates–Granger weights, and (e) Bates–Granger ranks are used as forecast combination techniques. In the relative majority of cases (i.e., in 13 of 28), combined forecasts based on Bates–Granger weights and on Bates–Granger ranks provide the highest level of forecast accuracy in terms of typical measures. Finally, the employed methodology represents a fully replicable toolkit for practitioners in terms of both forecast models and forecast combination techniques. Full article
(This article belongs to the Special Issue Tourism Forecasting: Time-Series Analysis of World and Regional Data)
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14 pages, 1849 KiB  
Article
COVID-19 and Tourism: Analyzing the Effects of COVID-19 Statistics and Media Coverage on Attitudes toward Tourism
by Maksim Godovykh, Jorge Ridderstaat, Carissa Baker and Alan Fyall
Forecasting 2021, 3(4), 870-883; https://doi.org/10.3390/forecast3040053 - 17 Nov 2021
Cited by 15 | Viewed by 5598
Abstract
COVID-19 has significantly influenced tourism, including tourists’ and residents’ attitudes toward tourism. At the same time, attitudes and consumer confidence are important for economic recovery in the tourism sector. This study explores the effects of the COVID-19 pandemic on people’s attitudes toward tourism [...] Read more.
COVID-19 has significantly influenced tourism, including tourists’ and residents’ attitudes toward tourism. At the same time, attitudes and consumer confidence are important for economic recovery in the tourism sector. This study explores the effects of the COVID-19 pandemic on people’s attitudes toward tourism by analyzing time-series data on the number of COVID-19 positive cases, vaccinations, news sentiment, a total number of daily mentions of tourism, and the share of voice for positive and negative sentiment toward tourism. The applied data analysis techniques include descriptive analysis, visual representation of data, data decomposition into trend and cycle components, unit root tests, Granger causality test, and multiple time series regression. The results demonstrate that the COVID-19 statistics and media coverage have significant effects on interest in tourism in general, as well as the positive and negative sentiment toward tourism. The results contribute to knowledge and practice by describing the effects of the disease statistics on attitudes toward tourism, introducing social media sentiment analysis as an opportunity to measure positive and negative sentiment toward tourism, and providing recommendations for government authorities, destination management organizations, and tourism providers. Full article
(This article belongs to the Special Issue Tourism Forecasting: Time-Series Analysis of World and Regional Data)
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2 pages, 190 KiB  
Editorial
Forecasting with Machine Learning Techniques
by Walayat Hussain, Asma Musabah Alkalbani and Honghao Gao
Forecasting 2021, 3(4), 868-869; https://doi.org/10.3390/forecast3040052 - 16 Nov 2021
Cited by 3 | Viewed by 2847
Abstract
The decision-maker is increasingly utilising machine learning (ML) techniques to find patterns in huge quantities of real-time data [...] Full article
(This article belongs to the Special Issue Forecasting with Machine Learning Techniques)
18 pages, 8496 KiB  
Article
Landslide Forecast by Time Series Modeling and Analysis of High-Dimensional and Non-Stationary Ground Motion Data
by Guoqi Qian, Antoinette Tordesillas and Hangfei Zheng
Forecasting 2021, 3(4), 850-867; https://doi.org/10.3390/forecast3040051 - 12 Nov 2021
Cited by 1 | Viewed by 2364
Abstract
High-dimensional, non-stationary vector time-series data are often seen in ground motion monitoring of geo-hazard events, e.g., landslides. For timely and reliable forecasts from such data, we developed a new statistical approach based on two advanced econometric methods, i.e., error-correction cointegration (ECC) and vector [...] Read more.
High-dimensional, non-stationary vector time-series data are often seen in ground motion monitoring of geo-hazard events, e.g., landslides. For timely and reliable forecasts from such data, we developed a new statistical approach based on two advanced econometric methods, i.e., error-correction cointegration (ECC) and vector autoregression (VAR), and a newly developed dimension reduction technique named empirical dynamic quantiles (EDQ). Our ECC–VAR–EDQ method was born by analyzing a big landslide dataset, comprising interferometric synthetic-aperture radar (InSAR) measurements of ground displacement that were observed at 5090 time states and 1803 locations on a slope. The aim was to develop an early warning system for reliably forecasting any impending slope failure whenever a precursory slope deformation is on the horizon. Specifically, we first reduced the spatial dimension of the observed landslide data by representing them as a small set of EDQ series with negligible loss of information. We then used the ECC–VAR model to optimally fit these EDQ series, from which forecasts of future ground motion can be efficiently computed. Moreover, our method is able to assess the future landslide risk by computing the relevant probability of ground motion to exceed a red-alert threshold level at each future time state and location. Applying the ECC–VAR–EDQ method to the motivating landslide data gives a prediction of the incoming slope failure more than 8 days in advance. Full article
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11 pages, 1159 KiB  
Article
Bootstrapped Holt Method with Autoregressive Coefficients Based on Harmony Search Algorithm
by Eren Bas, Erol Egrioglu and Ufuk Yolcu
Forecasting 2021, 3(4), 839-849; https://doi.org/10.3390/forecast3040050 - 4 Nov 2021
Cited by 4 | Viewed by 2223
Abstract
Exponential smoothing methods are one of the classical time series forecasting methods. It is well known that exponential smoothing methods are powerful forecasting methods. In these methods, exponential smoothing parameters are fixed on time, and they should be estimated with efficient optimization algorithms. [...] Read more.
Exponential smoothing methods are one of the classical time series forecasting methods. It is well known that exponential smoothing methods are powerful forecasting methods. In these methods, exponential smoothing parameters are fixed on time, and they should be estimated with efficient optimization algorithms. According to the time series component, a suitable exponential smoothing method should be preferred. The Holt method can produce successful forecasting results for time series that have a trend. In this study, the Holt method is modified by using time-varying smoothing parameters instead of fixed on time. Smoothing parameters are obtained for each observation from first-order autoregressive models. The parameters of the autoregressive models are estimated by using a harmony search algorithm, and the forecasts are obtained with a subsampling bootstrap approach. The main contribution of the paper is to consider the time-varying smoothing parameters with autoregressive equations and use the bootstrap method in an exponential smoothing method. The real-world time series are used to show the forecasting performance of the proposed method. Full article
(This article belongs to the Special Issue Feature Papers of Forecasting 2021)
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35 pages, 18834 KiB  
Article
Different Forecasting Horizons Based Performance Analysis of Electricity Load Forecasting Using Multilayer Perceptron Neural Network
by Manogaran Madhiarasan and Mohamed Louzazni
Forecasting 2021, 3(4), 804-838; https://doi.org/10.3390/forecast3040049 - 2 Nov 2021
Cited by 4 | Viewed by 2854
Abstract
With an uninterrupted power supply to the consumer, it is obligatory to balance the electricity generated by the electricity load. The effective planning of economic dispatch, reserve requirements, and quality power provision for accurate consumer information concerning the electricity load is needed. The [...] Read more.
With an uninterrupted power supply to the consumer, it is obligatory to balance the electricity generated by the electricity load. The effective planning of economic dispatch, reserve requirements, and quality power provision for accurate consumer information concerning the electricity load is needed. The burden on the power system engineers eased electricity load forecasting is essential to ensure the enhanced power system operation and planning for reliable power provision. Fickle nature, atmospheric parameters influence makes electricity load forecasting a very complex and challenging task. This paper proposed a multilayer perceptron neural network (MLPNN) with an association of recursive fine-tuning strategy-based different forecasting horizons model for electricity load forecasting. We consider the atmospheric parameters as the inputs to the proposed model, overcoming the atmospheric effect on electricity load forecasting. Hidden layers and hidden neurons based on performance investigation performed. Analyzed performance of the proposed model with other existing models; the comparative performance investigation reveals that the proposed forecasting model performs rigorous with a minimal evaluation index (mean square error (MSE) of 1.1506 × 10−05 for Dataset 1 and MSE of 4.0142 × 10−07 for Dataset 2 concern to the single hidden layer and MSE of 2.9962 × 10−07 for Dataset 1, and MSE of 1.0425 × 10−08 for Dataset 2 concern to two hidden layers based proposed model) and compared to the considered existing models. The proposed neural network possesses a good forecasting ability because we develop based on various atmospheric parameters as the input variables, which overcomes the variance. It has a generic performance capability for electricity load forecasting. The proposed model is robust and more reliable. Full article
(This article belongs to the Special Issue Advanced Forecasting Methods with Applications to Smart Grids)
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30 pages, 5597 KiB  
Article
Forecasting Internal Migration in Russia Using Google Trends: Evidence from Moscow and Saint Petersburg
by Dean Fantazzini, Julia Pushchelenko, Alexey Mironenkov and Alexey Kurbatskii
Forecasting 2021, 3(4), 774-803; https://doi.org/10.3390/forecast3040048 - 28 Oct 2021
Cited by 5 | Viewed by 3558
Abstract
This paper examines the suitability of Google Trends data for the modeling and forecasting of interregional migration in Russia. Monthly migration data, search volume data, and macro variables are used with a set of univariate and multivariate models to study the migration data [...] Read more.
This paper examines the suitability of Google Trends data for the modeling and forecasting of interregional migration in Russia. Monthly migration data, search volume data, and macro variables are used with a set of univariate and multivariate models to study the migration data of the two Russian cities with the largest migration inflows: Moscow and Saint Petersburg. The empirical analysis does not provide evidence that the more people search online, the more likely they are to relocate to other regions. However, the inclusion of Google Trends data in a model improves the forecasting of the migration flows, because the forecasting errors are lower for models with internet search data than for models without them. These results also hold after a set of robustness checks that consider multivariate models able to deal with potential parameter instability and with a large number of regressors. Full article
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11 pages, 1363 KiB  
Article
Assessing Goodness of Fit for Verifying Probabilistic Forecasts
by Tae-Ho Kang, Ashish Sharma and Lucy Marshall
Forecasting 2021, 3(4), 763-773; https://doi.org/10.3390/forecast3040047 - 27 Oct 2021
Cited by 6 | Viewed by 2865
Abstract
The verification of probabilistic forecasts in hydro-climatology is integral to their development, use, and adoption. We propose here a means of utilizing goodness of fit measures for verifying the reliability of probabilistic forecasts. The difficulty in measuring the goodness of fit for a [...] Read more.
The verification of probabilistic forecasts in hydro-climatology is integral to their development, use, and adoption. We propose here a means of utilizing goodness of fit measures for verifying the reliability of probabilistic forecasts. The difficulty in measuring the goodness of fit for a probabilistic prediction or forecast is that predicted probability distributions for a target variable are not stationary in time, meaning one observation alone exists to quantify goodness of fit for each prediction issued. Therefore, we suggest an additional dissociation that can dissociate target information from the other time variant part—the target to be verified in this study is the alignment of observations to the predicted probability distribution. For this dissociation, the probability integral transformation is used. To measure the goodness of fit for the predicted probability distributions, this study uses the root mean squared deviation metric. If the observations after the dissociation can be assumed to be independent, the mean square deviation metric becomes a chi-square test statistic, which enables statistically testing the hypothesis regarding whether the observations are from the same population as the predicted probability distributions. An illustration of our proposed rationale is provided using the multi-model ensemble prediction for El Niño–Southern Oscillation. Full article
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22 pages, 3372 KiB  
Article
Examining Deep Learning Architectures for Crime Classification and Prediction
by Panagiotis Stalidis, Theodoros Semertzidis and Petros Daras
Forecasting 2021, 3(4), 741-762; https://doi.org/10.3390/forecast3040046 - 12 Oct 2021
Cited by 20 | Viewed by 5047
Abstract
In this paper, a detailed study on crime classification and prediction using deep learning architectures is presented. We examine the effectiveness of deep learning algorithms in this domain and provide recommendations for designing and training deep learning systems for predicting crime areas, using [...] Read more.
In this paper, a detailed study on crime classification and prediction using deep learning architectures is presented. We examine the effectiveness of deep learning algorithms in this domain and provide recommendations for designing and training deep learning systems for predicting crime areas, using open data from police reports. Having time-series of crime types per location as training data, a comparative study of 10 state-of-the-art methods against 3 different deep learning configurations is conducted. In our experiments with 5 publicly available datasets, we demonstrate that the deep learning-based methods consistently outperform the existing best-performing methods. Moreover, we evaluate the effectiveness of different parameters in the deep learning architectures and give insights for configuring them to achieve improved performance in crime classification and finally crime prediction. Full article
(This article belongs to the Special Issue Improved Forecasting through Artificial Intelligence)
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12 pages, 348 KiB  
Article
Bayesian Forecasting of Dynamic Extreme Quantiles
by Douglas E. Johnston
Forecasting 2021, 3(4), 729-740; https://doi.org/10.3390/forecast3040045 - 11 Oct 2021
Viewed by 2045
Abstract
In this paper, we provide a novel Bayesian solution to forecasting extreme quantile thresholds that are dynamic in nature. This is an important problem in many fields of study including climatology, structural engineering, and finance. We utilize results from extreme value theory to [...] Read more.
In this paper, we provide a novel Bayesian solution to forecasting extreme quantile thresholds that are dynamic in nature. This is an important problem in many fields of study including climatology, structural engineering, and finance. We utilize results from extreme value theory to provide the backdrop for developing a state-space model for the unknown parameters of the observed time-series. To solve for the requisite probability densities, we derive a Rao-Blackwellized particle filter and, most importantly, a computationally efficient, recursive solution. Using the filter, the predictive distribution of future observations, conditioned on the past data, is forecast at each time-step and used to compute extreme quantile levels. We illustrate the improvement in forecasting ability, versus traditional methods, using simulations and also apply our technique to financial market data. Full article
(This article belongs to the Special Issue Bayesian Time Series Forecasting)
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13 pages, 385 KiB  
Article
On the Autoregressive Time Series Model Using Real and Complex Analysis
by Torsten Ullrich
Forecasting 2021, 3(4), 716-728; https://doi.org/10.3390/forecast3040044 - 11 Oct 2021
Cited by 10 | Viewed by 3837
Abstract
The autoregressive model is a tool used in time series analysis to describe and model time series data. Its main structure is a linear equation using the previous values to compute the next time step; i.e., the short time relationship is the core [...] Read more.
The autoregressive model is a tool used in time series analysis to describe and model time series data. Its main structure is a linear equation using the previous values to compute the next time step; i.e., the short time relationship is the core component of the autoregressive model. Therefore, short-term effects can be modeled in an easy way, but the global structure of the model is not obvious. However, this global structure is a crucial aid in the model selection process in data analysis. If the global properties are not reflected in the data, a corresponding model is not compatible. This helpful knowledge avoids unsuccessful modeling attempts. This article analyzes the global structure of the autoregressive model through the derivation of a closed form. In detail, the closed form of an autoregressive model consists of the basis functions of a fundamental system of an ordinary differential equation with constant coefficients; i.e., it consists of a combination of polynomial factors with sinusoidal, cosinusoidal, and exponential functions. This new insight supports the model selection process. Full article
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21 pages, 3046 KiB  
Article
Deterministic-Probabilistic Approach to Predict Lightning-Caused Forest Fires in Mounting Areas
by Nikolay Baranovskiy
Forecasting 2021, 3(4), 695-715; https://doi.org/10.3390/forecast3040043 - 27 Sep 2021
Cited by 7 | Viewed by 2484
Abstract
Forest fires from lightnings create a tense situation in various regions of states with forested areas. It is noted that in mountainous areas this is especially important in view of the geophysical processes of lightning activity. The aim of the study is to [...] Read more.
Forest fires from lightnings create a tense situation in various regions of states with forested areas. It is noted that in mountainous areas this is especially important in view of the geophysical processes of lightning activity. The aim of the study is to develop a deterministic-probabilistic approach to predicting forest fire danger due to lightning activity in mountainous regions. To develop a mathematical model, the main provisions of the theory of probability and mathematical statistics, as well as the general theory of heat transfer, were used. The scientific novelty of the research is due to the complex use of probabilistic criteria and deterministic mathematical models of tree ignition by a cloud-to-ground lightning discharge. The paper presents probabilistic criteria for predicting forest fire danger, taking into account the lightning activity, meteorological data, and forest growth conditions, as well as deterministic mathematical models of ignition of deciduous and coniferous trees by electric current of a cloud-to-ground lightning discharge. The work uses synthetic data on the discharge parameters and characteristics of the forest-covered area, which correspond to the forest fire situation in the Republic of Altay and the Republic of Buryatia (Russian Federation). The dependences of the probability for occurrence of forest fires on various parameters have been obtained. Full article
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13 pages, 931 KiB  
Article
A Real-Time Data Analysis Platform for Short-Term Water Consumption Forecasting with Machine Learning
by Aida Boudhaouia and Patrice Wira
Forecasting 2021, 3(4), 682-694; https://doi.org/10.3390/forecast3040042 - 26 Sep 2021
Cited by 9 | Viewed by 4038
Abstract
This article presents a real-time data analysis platform to forecast water consumption with Machine-Learning (ML) techniques. The strategy fully relies on a web-oriented architecture to ensure better management and optimized monitoring of water consumption. This monitoring is carried out through a communicating system [...] Read more.
This article presents a real-time data analysis platform to forecast water consumption with Machine-Learning (ML) techniques. The strategy fully relies on a web-oriented architecture to ensure better management and optimized monitoring of water consumption. This monitoring is carried out through a communicating system for collecting data in the form of unevenly spaced time series. The platform is completed by learning capabilities to analyze and forecast water consumption. The analysis consists of checking the data integrity and inconsistency, in looking for missing data, and in detecting abnormal consumption. Forecasting is based on the Long Short-Term Memory (LSTM) and the Back-Propagation Neural Network (BPNN). After evaluation, results show that the ML approaches can predict water consumption without having prior knowledge about the data and the users. The LSTM approach, by being able to grab the long-term dependencies between time steps of water consumption, allows the prediction of the amount of consumed water in the next hour with an error of some liters and the instants of the 5 next consumed liters in some milliseconds. Full article
(This article belongs to the Special Issue Feature Papers of Forecasting 2021)
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19 pages, 2185 KiB  
Article
Battery Sizing for Different Loads and RES Production Scenarios through Unsupervised Clustering Methods
by Alfredo Nespoli, Andrea Matteri, Silvia Pretto, Luca De Ciechi and Emanuele Ogliari
Forecasting 2021, 3(4), 663-681; https://doi.org/10.3390/forecast3040041 - 24 Sep 2021
Cited by 1 | Viewed by 2307
Abstract
The increasing penetration of Renewable Energy Sources (RESs) in the energy mix is determining an energy scenario characterized by decentralized power production. Between RESs power generation technologies, solar PhotoVoltaic (PV) systems constitute a very promising option, but their production is not programmable due [...] Read more.
The increasing penetration of Renewable Energy Sources (RESs) in the energy mix is determining an energy scenario characterized by decentralized power production. Between RESs power generation technologies, solar PhotoVoltaic (PV) systems constitute a very promising option, but their production is not programmable due to the intermittent nature of solar energy. The coupling between a PV facility and a Battery Energy Storage System (BESS) allows to achieve a greater flexibility in power generation. However, the design phase of a PV+BESS hybrid plant is challenging due to the large number of possible configurations. The present paper proposes a preliminary procedure aimed at predicting a family of batteries which is suitable to be coupled with a given PV plant configuration. The proposed procedure is applied to new hypothetical plants built to fulfill the energy requirements of a commercial and an industrial load. The energy produced by the PV system is estimated on the basis of a performance analysis carried out on similar real plants. The battery operations are established through two decision-tree-like structures regulating charge and discharge respectively. Finally, an unsupervised clustering is applied to all the possible PV+BESS configurations in order to identify the family of feasible solutions. Full article
(This article belongs to the Special Issue Feature Papers of Forecasting 2021)
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