Special Issue "Feature Papers of Forecasting"

A special issue of Forecasting (ISSN 2571-9394).

Deadline for manuscript submissions: 31 December 2020.

Special Issue Editor

Special Issue Information

Dear Colleagues,

As Editor-in-Chief of Forecasting, I am glad to announce the Special Issue titled "Feature Papers". This Special Issue is designed to publish high-quality papers in Forecasting. We welcome submissions from Editorial Board Members and from outstanding scholars invited by the Editorial Board and the Editorial Office. The scope of this Special Issue includes, but is not limited to, the following topics: power and energy forecasting; forecasting in economics and management; forecasting in computer science; weather and forecasting; and environmental forecasting.

We will select 10–20 papers in 2020 from excellent scholars around the world to publish for free in order to benefit both authors and readers.

You are welcome to send short proposals for submissions of feature papers to our Editorial Office ([email protected]). They will first be evaluated by academic editors, and then, selected papers will be thoroughly and rigorously peer reviewed.

Prof. Dr. Sonia Leva
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Forecasting is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • power and energy forecasting
  • forecasting in economics and management
  • forecasting in computer science
  • weather and forecasting
  • environmental forecasting

Published Papers (9 papers)

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Research

Open AccessArticle
Photovoltaic Output Power Estimation and Baseline Prediction Approach for a Residential Distribution Network with Behind-the-Meter Systems
Forecasting 2020, 2(4), 470-487; https://doi.org/10.3390/forecast2040025 - 02 Nov 2020
Abstract
Considering that most of the photovoltaic (PV) data are behind-the-meter (BTM), there is a great challenge to implement effective demand response projects and make a precise customer baseline (CBL) prediction. To solve the problem, this paper proposes a data-driven PV output power estimation [...] Read more.
Considering that most of the photovoltaic (PV) data are behind-the-meter (BTM), there is a great challenge to implement effective demand response projects and make a precise customer baseline (CBL) prediction. To solve the problem, this paper proposes a data-driven PV output power estimation approach using only net load data, temperature data, and solar irradiation data. We first obtain the relationship between delta actual load and delta temperature by calculating the delta net load from matching the net load of irradiation for an approximate day with the least squares method. Then we match and make a difference of the net load with similar electricity consumption behavior to establish the relationship between delta PV output power and delta irradiation. Finally, we get the PV output power and implement PV-load decoupling by modifying the relationship between delta PV and delta irradiation. The case studies verify the effectiveness of the approach and it provides an important reference to perform PV-load decoupling and CBL prediction in a residential distribution network with BTM PV systems. Full article
(This article belongs to the Special Issue Feature Papers of Forecasting)
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Open AccessArticle
Application of a Semi-Empirical Dynamic Model to Forecast the Propagation of the COVID-19 Epidemics in Spain
Forecasting 2020, 2(4), 452-469; https://doi.org/10.3390/forecast2040024 - 22 Oct 2020
Abstract
A semiempirical model, based in the logistic map, was developed to forecast the different phases of the COVID-19 epidemic. This paper shows the mathematical model and a proposal for its calibration. Specific results are shown for Spain. Four phases were considered: non-controlled evolution; [...] Read more.
A semiempirical model, based in the logistic map, was developed to forecast the different phases of the COVID-19 epidemic. This paper shows the mathematical model and a proposal for its calibration. Specific results are shown for Spain. Four phases were considered: non-controlled evolution; total lock-down; partial easing of the lock-down; and a phased lock-down easing. For no control the model predicted the infection of a 25% of the Spanish population, 1 million would need intensive care and 700,000 direct deaths. For total lock-down the model predicted 194,000 symptomatic infected, 85,700 hospitalized, 8600 patients needing an Intensive Care Unit (ICU) and 19,500 deaths. The peak was predicted between the 29 March/3 April. For the third phase, with a daily rate r=1.03, the model predicted 400,000 infections and 46,000±15,000 deaths. The real r was below 1%, and a revision with updated parameters provided a prediction of 250,000 infected and 29,000±15,000 deaths. The reported values by the end of May were 282,870 infected and 28,552 deaths. After easing of the lock-down the model predicted that the health system would not saturate if r was kept below 1.02. This model provided good accuracy during epidemics development. Full article
(This article belongs to the Special Issue Feature Papers of Forecasting)
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Open AccessArticle
Cost Estimating Using a New Learning Curve Theory for Non-Constant Production Rates
Forecasting 2020, 2(4), 429-451; https://doi.org/10.3390/forecast2040023 - 16 Oct 2020
Abstract
Traditional learning curve theory assumes a constant learning rate regardless of the number of units produced. However, a collection of theoretical and empirical evidence indicates that learning rates decrease as more units are produced in some cases. These diminishing learning rates cause traditional [...] Read more.
Traditional learning curve theory assumes a constant learning rate regardless of the number of units produced. However, a collection of theoretical and empirical evidence indicates that learning rates decrease as more units are produced in some cases. These diminishing learning rates cause traditional learning curves to underestimate required resources, potentially resulting in cost overruns. A diminishing learning rate model, namely Boone’s learning curve, was recently developed to model this phenomenon. This research confirms that Boone’s learning curve systematically reduced error in modeling observed learning curves using production data from 169 Department of Defense end-items. However, high amounts of variability in error reduction precluded concluding the degree to which Boone’s learning curve reduced error on average. This research further justifies the necessity of a diminishing learning rate forecasting model and assesses a potential solution to model diminishing learning rates. Full article
(This article belongs to the Special Issue Feature Papers of Forecasting)
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Open AccessArticle
Bus Travel Time: Experimental Evidence and Forecasting
Forecasting 2020, 2(3), 309-322; https://doi.org/10.3390/forecast2030017 - 28 Aug 2020
Abstract
Bus travel time analysis plays a key role in transit operation planning, and methods are needed for investigating its variability and for forecasting need. Nowadays, telematics is opening up new opportunities, given that large datasets can be gathered through automated monitoring, and this [...] Read more.
Bus travel time analysis plays a key role in transit operation planning, and methods are needed for investigating its variability and for forecasting need. Nowadays, telematics is opening up new opportunities, given that large datasets can be gathered through automated monitoring, and this topic can be studied in more depth with new experimental evidence. The paper proposes a time-series-based approach for travel time forecasting, and data from automated vehicle monitoring (AVM) of bus lines sharing the road lanes with other traffic in Rome (Italy) and Lviv (Ukraine) are used. The results show the goodness of such an approach for the analysis and reliable forecasts of bus travel times. The similarities and dissimilarities in terms of travel time patterns and city structure were also pointed out, showing the need to take them into account when developing forecasting methods. Full article
(This article belongs to the Special Issue Feature Papers of Forecasting)
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Open AccessArticle
A Generalized Flow for B2B Sales Predictive Modeling: An Azure Machine-Learning Approach
Forecasting 2020, 2(3), 267-283; https://doi.org/10.3390/forecast2030015 - 06 Aug 2020
Abstract
Predicting the outcome of sales opportunities is a core part of successful business management. Conventionally, undertaking this prediction has relied mostly on subjective human evaluations in the process of sales decision-making. In this paper, we addressed the problem of forecasting the outcome of [...] Read more.
Predicting the outcome of sales opportunities is a core part of successful business management. Conventionally, undertaking this prediction has relied mostly on subjective human evaluations in the process of sales decision-making. In this paper, we addressed the problem of forecasting the outcome of Business to Business (B2B) sales by proposing a thorough data-driven Machine-Learning (ML) workflow on a cloud-based computing platform: Microsoft Azure Machine-Learning Service (Azure ML). This workflow consists of two pipelines: (1) An ML pipeline to train probabilistic predictive models on the historical sales opportunities data. In this pipeline, data is enriched with an extensive feature enhancement step and then used to train an ensemble of ML classification models in parallel. (2) A prediction pipeline to use the trained ML model and infer the likelihood of winning new sales opportunities along with calculating optimal decision boundaries. The effectiveness of the proposed workflow was evaluated on a real sales dataset of a major global B2B consulting firm. Our results implied that decision-making based on the ML predictions is more accurate and brings a higher monetary value. Full article
(This article belongs to the Special Issue Feature Papers of Forecasting)
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Open AccessArticle
Benchmarking Real-Time Streamflow Forecast Skill in the Himalayan Region
Forecasting 2020, 2(3), 230-247; https://doi.org/10.3390/forecast2030013 - 08 Jul 2020
Abstract
Improving decision-making in various areas of water policy and management (e.g., flood and drought preparedness, reservoir operation and hydropower generation) requires skillful streamflow forecasts. Despite the recent advances in hydrometeorological prediction, real-time streamflow forecasting over the Himalayas remains a critical issue and challenge, [...] Read more.
Improving decision-making in various areas of water policy and management (e.g., flood and drought preparedness, reservoir operation and hydropower generation) requires skillful streamflow forecasts. Despite the recent advances in hydrometeorological prediction, real-time streamflow forecasting over the Himalayas remains a critical issue and challenge, especially with complex basin physiography, shifting weather patterns and sparse and biased in-situ hydrometeorological monitoring data. In this study, we demonstrate the utility of low-complexity data-driven persistence-based approaches for skillful streamflow forecasting in the Himalayan country Nepal. The selected approaches are: (1) simple persistence, (2) streamflow climatology and (3) anomaly persistence. We generated the streamflow forecasts for 65 stream gauge stations across Nepal for short-to-medium range forecast lead times (1 to 12 days). The selected gauge stations were monitored by the Department of Hydrology and Meteorology (DHM) Nepal, and they represent a wide range of basin size, from ~17 to ~54,100 km2. We find that the performance of persistence-based forecasting approaches depends highly upon the lead time, flow threshold, basin size and flow regime. Overall, the persistence-based forecast results demonstrate higher forecast skill in snow-fed rivers over intermittent ones, moderate flows over extreme ones and larger basins over smaller ones. The streamflow forecast skill obtained in this study can serve as a benchmark (reference) for the evaluation of many operational forecasting systems over the Himalayas. Full article
(This article belongs to the Special Issue Feature Papers of Forecasting)
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Open AccessArticle
Are Combined Tourism Forecasts Better at Minimizing Forecasting Errors?
Forecasting 2020, 2(3), 211-229; https://doi.org/10.3390/forecast2030012 - 29 Jun 2020
Abstract
This study, which was contracted by the European Commission and is geared towards easy replicability by practitioners, compares the accuracy of individual and combined approaches to forecasting tourism demand for the total European Union. The evaluation of the forecasting accuracies was performed recursively [...] Read more.
This study, which was contracted by the European Commission and is geared towards easy replicability by practitioners, compares the accuracy of individual and combined approaches to forecasting tourism demand for the total European Union. The evaluation of the forecasting accuracies was performed recursively (i.e., based on expanding estimation windows) for eight quarterly periods spanning two years in order to check the stability of the outcomes during a changing macroeconomic environment. The study sample includes Eurostat data from January 2005 until August 2017, and out of sample forecasts were calculated for the last two years for three and six months ahead. The analysis of the out-of-sample forecasts for arrivals and overnights showed that forecast combinations taking the historical forecasting performance of individual approaches such as Autoregressive Integrated Moving Average (ARIMA) models, REGARIMA models with different trend variables, and Error Trend Seasonal (ETS) models into account deliver the best results. Full article
(This article belongs to the Special Issue Feature Papers of Forecasting)
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Open AccessArticle
Performance Comparison between Deep Learning and Optical Flow-Based Techniques for Nowcast Precipitation from Radar Images
Forecasting 2020, 2(2), 194-210; https://doi.org/10.3390/forecast2020011 - 24 Jun 2020
Cited by 1
Abstract
In this article, a nowcasting technique for meteorological radar images based on a generative neural network is presented. This technique’s performance is compared with state-of-the-art optical flow procedures. Both methods have been validated using a public domain data set of radar images, covering [...] Read more.
In this article, a nowcasting technique for meteorological radar images based on a generative neural network is presented. This technique’s performance is compared with state-of-the-art optical flow procedures. Both methods have been validated using a public domain data set of radar images, covering an area of about 104 km2 over Japan, and a period of five years with a sampling frequency of five minutes. The performance of the neural network, trained with three of the five years of data, forecasts with a time horizon of up to one hour, evaluated over one year of the data, proved to be significantly better than those obtained with the techniques currently in use. Full article
(This article belongs to the Special Issue Feature Papers of Forecasting)
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Open AccessArticle
Assessment of Direct Normal Irradiance Forecasts Based on IFS/ECMWF Data and Observations in the South of Portugal
Forecasting 2020, 2(2), 130-150; https://doi.org/10.3390/forecast2020007 - 16 May 2020
Cited by 1
Abstract
Direct Normal Irradiance (DNI) predictions obtained from the Integrated Forecasting System of the European Centre for Medium-Range Weather Forecast (IFS/ECMWF) were compared against ground-based observational data for one location at the south of Portugal (Évora). Hourly and daily DNI values were analyzed for [...] Read more.
Direct Normal Irradiance (DNI) predictions obtained from the Integrated Forecasting System of the European Centre for Medium-Range Weather Forecast (IFS/ECMWF) were compared against ground-based observational data for one location at the south of Portugal (Évora). Hourly and daily DNI values were analyzed for different temporal forecast horizons (1 to 3 days ahead) and results show that the IFS/ECMWF slightly overestimates DNI for the period of analysis (1 August 2018 until 31 July 2019) with a fairly good agreement between model and observations. Hourly basis evaluation shows relatively high errors, independently of the forecast day. Root mean square error increases as the forecast time increases with a relative error of ~45% between the first and the last forecast. Similar patterns are observed in the daily analysis with comparable magnitude errors. The correlation coefficients between forecast and observed data are above 0.7 for both hourly and daily data. A methodology based on a new DNI attenuation Index (DAI) was developed to estimate cloud fraction from hourly values integrated over a day and, with that, to correlate the accuracy of the forecast with sky conditions. This correlation with DAI reveals that in IFS/ECMWF model, the atmosphere as being more transparent than reality since cloud cover is underestimated in the majority of the months of the year, taking the ground-based measurements as a reference. The use of the DAI estimator confirms that the errors in IFS/ECMWF are larger under cloudy skies than under clear sky. The development and application of a post-processing methodology improves the DNI predictions from the IFS/ECMWF outputs, with a decrease of error of the order of ~30%, when compared with raw data. Full article
(This article belongs to the Special Issue Feature Papers of Forecasting)
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