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

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 36968

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Prof. Dr. Sonia Leva

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Guest Editor

Department of Energy, Politecnico di Milano, 20156 Milan, Italy
Interests: photovoltaic system; grid; power sharing; inverters; forecasting; nowcasting; machine learning; degradation; battery management systems; polymer solar cells; organic photovoltaics; electric vehicle; vehicle-to-grid; microgrid; energy systems; maximum power point trackers; electric power plant loads; electricity price; power markets; heterogeneous networks; base stations; energy efficiency; life cycle assessment; wind power; regenerative braking; bicycles; motorcycles; car sharing; autonomous vehicles
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Special Issue Information

Dear Colleagues,

As Editor-in-Chief of Forecasting, I am glad to announce the Special Issue "Feature Papers of Forecasting 2022". This Special Issue is designed to publish high-quality papers in Forecasting. We welcome submissions from Editorial Board Members and 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 2022 from excellent scholars around the world to publish for free for the benefit of 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 submissions that pass pre-check are 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 1800 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.

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Published Papers (10 papers)

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Research

15 pages, 1855 KiB

Open AccessArticle

Short-Term Probabilistic Load Forecasting in University Buildings by Means of Artificial Neural Networks

by Carla Sahori Seefoo Jarquin, Alessandro Gandelli, Francesco Grimaccia and Marco Mussetta

Forecasting 2023, 5(2), 390-404; https://doi.org/10.3390/forecast5020021 - 13 Apr 2023

Cited by 5 | Viewed by 2300

Abstract

Understanding how, why and when energy consumption changes provides a tool for decision makers throughout the power networks. Thus, energy forecasting provides a great service. This research proposes a probabilistic approach to capture the five inherent dimensions of a forecast: three dimensions in space, time and probability. The forecasts are generated through different models based on artificial neural networks as a post-treatment of point forecasts based on shallow artificial neural networks, creating a dynamic ensemble. The singular value decomposition (SVD) technique is then used herein to generate temperature scenarios and project different futures for the probabilistic forecast. In additional to meteorological conditions, time and recency effects were considered as predictor variables. Buildings that are part of a university campus are used as a case study. Though this methodology was applied to energy demand forecasts in buildings alone, it can easily be extended to energy communities as well. Full article

(This article belongs to the Special Issue Feature Papers of Forecasting 2022)

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16 pages, 1393 KiB

Open AccessArticle

A Day-Ahead Photovoltaic Power Prediction via Transfer Learning and Deep Neural Networks

by Seyed Mahdi Miraftabzadeh, Cristian Giovanni Colombo, Michela Longo and Federica Foiadelli

Forecasting 2023, 5(1), 213-228; https://doi.org/10.3390/forecast5010012 - 17 Feb 2023

Cited by 21 | Viewed by 4682

Abstract

Climate change and global warming drive many governments and scientists to investigate new renewable and green energy sources. Special attention is on solar panel technology, since solar energy is considered one of the primary renewable sources and solar panels can be installed in domestic neighborhoods. Photovoltaic (PV) power prediction is essential to match supply and demand and ensure grid stability. However, the PV system has assertive stochastic behavior, requiring advanced forecasting methods, such as machine learning and deep learning, to predict day-ahead PV power accurately. Machine learning models need a rich historical dataset that includes years of PV power outputs to capture hidden patterns between essential variables to predict day-ahead PV power production accurately. Therefore, this study presents a framework based on the transfer learning method to use reliable trained deep learning models of old PV plants in newly installed PV plants in the same neighborhoods. The numerical results show the effectiveness of transfer learning in day-ahead PV prediction in newly established PV plants where a sizable historical dataset of them is unavailable. Among all nine models presented in this study, the LSTM models have better performance in PV power prediction. The new LSTM model using the inadequate dataset has 0.55 mean square error (MSE) and 47.07% weighted mean absolute percentage error (wMAPE), while the transferred LSTM model improves prediction accuracy to 0.168 MSE and 32.04% wMAPE. Full article

(This article belongs to the Special Issue Feature Papers of Forecasting 2022)

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15 pages, 1250 KiB

Open AccessArticle

Comparison of ARIMA, SutteARIMA, and Holt-Winters, and NNAR Models to Predict Food Grain in India

by Ansari Saleh Ahmar, Pawan Kumar Singh, R. Ruliana, Alok Kumar Pandey and Stuti Gupta

Forecasting 2023, 5(1), 138-152; https://doi.org/10.3390/forecast5010006 - 10 Jan 2023

Cited by 5 | Viewed by 3956

Abstract

The agriculture sector plays an essential function within the Indian economic system. Foodgrains provide almost all the calories and proteins. This paper aims to compare ARIMA, SutteARIMA, Holt-Winters, and NNAR models to recommend an effective model to predict foodgrains production in India. The execution of the SutteARIMA predictive model used in this analysis was compared with the established ARIMA, Neural Network Auto-Regressive (NNAR), and Holt-Winters models, which have been widely applied for time series prediction. The findings of this study reveal that both the SutteARIMA model and the Holt-Winters model performed well with real-life problems and can effectively and profitably be engaged for food grain forecasting in India. The food grain forecasting approach with the SutteARIMA model indicated superior performance over the ARIMA, Holt-Winters, and NNAR models. Indeed, the actual and predicted values of the SutteARIMA and Holt-Winters forecasting models are quite close to predicting foodgrains production in India. This has been verified by MAPE and MSE values that are relatively low with the SutteARIMA model. Therefore, India’s SutteARIMA model was used to predict foodgrains production from 2021 to 2025. The forecasted amount of respective crops are as follows (in lakh tonnes) 1140.14 (wheat), 1232.27 (rice), 466.46 (coarse), 259.95 (pulses), and a total 3069.80 (foodgrains) by 2025. Full article

(This article belongs to the Special Issue Feature Papers of Forecasting 2022)

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21 pages, 3032 KiB

Open AccessArticle

Evaluating the Comparative Accuracy of COVID-19 Mortality Forecasts: An Analysis of the First-Wave Mortality Forecasts in the United States

by Rahul Pathak and Daniel Williams

Forecasting 2022, 4(4), 798-818; https://doi.org/10.3390/forecast4040044 - 29 Sep 2022

Cited by 1 | Viewed by 3054

Abstract

The sudden onset of the COVID-19 pandemic posed significant challenges for forecasting professionals worldwide. This article examines the early forecasts of COVID-19 transmission, using the context of the United States, one of the early epicenters of the crisis. The article compares the relative accuracy of selected models from two forecasters who informed government policy in the first three months of the pandemic, the Institute of Health Metrics and Evaluation (IHME) and Columbia University. Furthermore, we examine whether the forecasts improved as more data became available in the subsequent months of the pandemic, using the forecasts from Los Alamos National Laboratory and the University of Texas, Austin. The analysis focuses on mortality estimates and compares forecasts using epidemiological and curve-fitting models during the first wave of the pandemic from March 2020 to October 2020. As health agencies worldwide struggled with uncertainty in models and projections of COVID-19 caseload and mortality, this article provides important insights that can be useful for crafting policy responses to the ongoing pandemic and future outbreaks. Full article

(This article belongs to the Special Issue Feature Papers of Forecasting 2022)

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20 pages, 5566 KiB

Open AccessArticle

Influence of Car Configurator Webpage Data from Automotive Manufacturers on Car Sales by Means of Correlation and Forecasting

by Juan Manuel García Sánchez, Xavier Vilasís Cardona and Alexandre Lerma Martín

Forecasting 2022, 4(3), 634-653; https://doi.org/10.3390/forecast4030034 - 11 Jul 2022

Viewed by 3625

Abstract

A methodology to prove the influence of car configurator webpage data for automotive manufacturers is developed across this research. Firstly, the correlation between online data and sales is measured. Afterward, car variant sales are predicted using a set of forecasting techniques divided into univariate and multivariate ones. Finally, weekly color mix sales based on these techniques are built and compared. Results show that users visit car configurator webpages 1 to 6 months before the purchase date. Additionally, car variants predictions and weekly color mix sales derived from multivariate techniques, i.e., using car configurator data as external input, provide improvement up to 25 points in the assessment metric. Full article

(This article belongs to the Special Issue Feature Papers of Forecasting 2022)

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13 pages, 3355 KiB

Open AccessArticle

Estimating Path Choice Models through Floating Car Data

by Antonio Comi and Antonio Polimeni

Forecasting 2022, 4(2), 525-537; https://doi.org/10.3390/forecast4020029 - 3 Jun 2022

Cited by 9 | Viewed by 2831

Abstract

The path choice models play a key role in transportation engineering, especially when coupled with an assignment procedure allowing link flows to be obtained. Their implementation could be complex and resource-consuming. In particular, such a task consists of several stages, including (1) the collection of a large set of data from surveys to infer users’ path choices and (2) the definition of a model able to reproduce users’ choice behaviors. Nowadays, stage (1) can be improved using floating car data (FCD), which allow one to obtain a reliable dataset of paths. In relation to stage (2), different structures of models are available; however, a compromise has to be found between the model’s ability to reproduce the observed paths (including the ability to forecast the future path choices) and its applicability in real contexts (in addition to guaranteeing the robustness of the assignment procedure). Therefore, the aim of this paper is to explore the opportunities offered by FCD to calibrate a path/route choice model to be included in a general procedure for scenario assessment. The proposed methodology is applied to passenger and freight transport case studies. Significant results are obtained showing the opportunities offered by FCD in supporting path choice simulation. Moreover, the characteristics of the model make it easily applicable and exportable to other contexts. Full article

(This article belongs to the Special Issue Feature Papers of Forecasting 2022)

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23 pages, 1949 KiB

Open AccessArticle

Prevalence and Economic Costs of Absenteeism in an Aging Population—A Quasi-Stochastic Projection for Germany

by Patrizio Vanella, Christina Benita Wilke and Doris Söhnlein

Forecasting 2022, 4(1), 371-393; https://doi.org/10.3390/forecast4010021 - 15 Mar 2022

Cited by 4 | Viewed by 3236

Abstract

Demographic change is leading to the aging of German society. As long as the baby boom cohorts are still of working age, the working population will also age—and decline as soon as this baby boom generation gradually reaches retirement age. At the same time, there has been a trend toward increasing absenteeism (times of inability to work) in companies since the zero years, with the number of days of absence increasing with age. We present a novel stochastic forecast approach that combines population forecasting with forecasts of labor force participation trends, considering epidemiological aspects. For this, we combine a stochastic Monte Carlo-based cohort-component forecast of the population with projections of labor force participation rates and morbidity rates. This article examines the purely demographic effect on the economic costs associated with such absenteeism due to the inability to work. Under expected future employment patterns and constant morbidity patterns, absenteeism is expected to be close to 5 percent by 2050 relative to 2020, associated with increasing economic costs of almost 3 percent. Our results illustrate how strongly the pronounced baby boom/baby bust phenomenon determines demographic development in Germany in the midterm. Full article

(This article belongs to the Special Issue Feature Papers of Forecasting 2022)

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22 pages, 3464 KiB

Open AccessArticle

Application of Agent-Based Modeling in Agricultural Productivity in Rural Area of Bahir Dar, Ethiopia

by Sardorbek Musayev, Jonathan Mellor, Tara Walsh and Emmanouil Anagnostou

Forecasting 2022, 4(1), 349-370; https://doi.org/10.3390/forecast4010020 - 13 Mar 2022

Cited by 3 | Viewed by 3746

Abstract

Effective weather forecast information helps smallholder farmers improve their adaptation to climate uncertainties and crop productivity. The main objective of this study was to assess the impact of weather forecast adoption on crop productivity. We coupled agent-based and crop productivity models to study the impact of farmers’ management decisions on maize productivity under different rainfall scenarios in Ethiopia. A household survey was conducted with 100 households from 5 villages and was used to validate the crop model. The agent-based model (ABM) analyzed the farmers’ behaviors in crop management under different dry, wet, and normal rainfall conditions. ABM results and crop data from the survey were then used as input data sources for the crop model. Our results show that farming decisions based on weather forecast information improved yield productivity from 17% to 30% under dry and wet seasons, respectively. The impact of adoption rates due to farmers’ intervillage interactions, connections, radio, agriculture extension services, and forecast accuracy brought additional crop yields into the Kebele compared to non-forecast users. Our findings help local policy makers to understand the impact of the forecast information. Results of this study can be used to develop agricultural programs where rainfed agriculture is common. Full article

(This article belongs to the Special Issue Feature Papers of Forecasting 2022)

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11 pages, 7974 KiB

Open AccessArticle

Irradiance Nowcasting by Means of Deep-Learning Analysis of Infrared Images

by Alessandro Niccolai, Seyedamir Orooji, Andrea Matteri, Emanuele Ogliari and Sonia Leva

Forecasting 2022, 4(1), 338-348; https://doi.org/10.3390/forecast4010019 - 4 Mar 2022

Cited by 4 | Viewed by 2796

Abstract

This work proposes and evaluates a method for the nowcasting of solar irradiance variability in multiple time horizons, namely 5, 10, and 15 min ahead. The method is based on a Convolutional Neural Network structure that exploits infrared sky images acquired through an All-Sky Imager to estimate the range of possible values that the Clear-Sky Index will possibly assume over a selected forecast horizon. All data available, from the infrared images to the measurements of Global Horizontal Irradiance (necessary in order to compute Clear-Sky Index), are acquired at SolarTech^LAB in Politecnico di Milano. The proposed method demonstrated a discrete performance level, with an accuracy peak for the 5 min time horizon, where about 65% of the available samples are attributed to the correct range of Clear-Sky Index values. Full article

(This article belongs to the Special Issue Feature Papers of Forecasting 2022)

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16 pages, 516 KiB

Open AccessArticle

Short Term Electric Power Load Forecasting Using Principal Component Analysis and Recurrent Neural Networks

by Venkataramana Veeramsetty, Dongari Rakesh Chandra, Francesco Grimaccia and Marco Mussetta

Forecasting 2022, 4(1), 149-164; https://doi.org/10.3390/forecast4010008 - 24 Jan 2022

Cited by 36 | Viewed by 4871

Abstract

Electrical load forecasting study is required in electric power systems for different applications with respect to the specific time horizon, such as optimal operations, grid stability, Demand Side Management (DSM) and long-term strategic planning. In this context, machine learning and data analytics models represent a valuable tool to cope with the intrinsic complexity and especially design future demand-side advanced services. The main novelty in this paper is that the combination of a Recurrent Neural Network (RNN) and Principal Component Analysis (PCA) techniques is proposed to improve the forecasting capability of the hourly load on an electric power substation. A historical dataset of measured loads related to a 33/11 kV MV substation is considered in India as a case study, in order to properly validate the designed method. Based on the presented numerical results, the proposed approach proved itself to accurately predict loads with a reduced dimensionality of input data, thus minimizing the overall computational effort. Full article

(This article belongs to the Special Issue Feature Papers of Forecasting 2022)

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