New Challenges in Energy and Finance Forecasting in the Era of Big Data

A special issue of Forecasting (ISSN 2571-9394). This special issue belongs to the section "Power and Energy Forecasting".

Deadline for manuscript submissions: closed (30 August 2022) | Viewed by 14170

Special Issue Editors

Department of Statistical Sciences, University of Padua, 35121 Padova, Italy
Interests: electricity price and demand forecasting; robust statistics; time series forecasting
Special Issues, Collections and Topics in MDPI journals
Department of Statistical Sciences, University of Padova, Via Cesare Battisti, 241, 35121 Padova, Italy
Interests: energy transition modelling; decarbonization; innovation diffusion models; competition and cooperation dynamics; nonlinear modelling

Special Issue Information

Dear Colleagues,

Big data are becoming increasingly available in many areas. Energy and finance are two of the main research fields where the production of a massive amount of data gives rise to many issues and challenges requiring the development of new tools and models. Smart meters’ data, sensor networks, customer payments, credit history, satellite imagery, Internet of Things devices, and high-frequency trading are just a few examples of big data generators creating new research challenges. Private and public energy and finance companies are aiming to take advantage of big data analytics to optimize their performances and improve service delivery. Big data analytics play a crucial role in reducing energy consumption and improving energy efficiency in the energy sector and in supporting investment decisions in the financial industry. Proper treatment of data flows generated almost in real time and reliable short- and medium-term predictions give strong support to decision makers operating on energy and financial markets. This Special Issue aims at collecting original contributions containing new theoretical and/or empirical results in the context of energy and finance forecasting using big data. Mathematical, statistical, and econometric models are common tools adopted in forecasting procedures; however, effective alternative approaches are also welcome.

Dr. Luigi Grossi
Dr. Mariangela Guidolin
Guest Editors

Manuscript Submission Information

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Keywords

  • energy forecasting
  • energy prices
  • energy demand
  • big data
  • energy storage
  • greenhouse gasses
  • CO2 emissions
  • financial price
  • high-frequency data
  • financial econometrics

Published Papers (5 papers)

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Research

21 pages, 399 KiB  
Article
Machine Learning Models and Intra-Daily Market Information for the Prediction of Italian Electricity Prices
by Silvia Golia, Luigi Grossi and Matteo Pelagatti
Forecasting 2023, 5(1), 81-101; https://doi.org/10.3390/forecast5010003 - 30 Dec 2022
Viewed by 2029
Abstract
In this paper we assess how intra-day electricity prices can improve the prediction of zonal day-ahead wholesale electricity prices in Italy. We consider linear autoregressive models with exogenous variables (ARX) with and without interactions among predictors, and non-parametric models taken from the machine [...] Read more.
In this paper we assess how intra-day electricity prices can improve the prediction of zonal day-ahead wholesale electricity prices in Italy. We consider linear autoregressive models with exogenous variables (ARX) with and without interactions among predictors, and non-parametric models taken from the machine learning literature. In particular, we implement Random Forests and support vector machines, which should automatically capture the relevant interactions among predictors. Given the large number of predictors, ARX models are also estimated using LASSO regularization, which improves predictions when regressors are many and selects the important variables. In addition to zonal intra-day prices, among the predictors we include also the official demand forecasts and wind generation expectations. Our results show that the prediction performance of the simple ARX model is mostly superior to those of machine learning models. The analysis of the relevance of exogenous variables, using variable importance measures, reveals that intra-day market information successfully contributes to the forecasting performance, although the impact differs among the estimated models. Full article
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27 pages, 941 KiB  
Article
Analyzing and Forecasting Multi-Commodity Prices Using Variants of Mode Decomposition-Based Extreme Learning Machine Hybridization Approach
by Emmanuel Senyo Fianu
Forecasting 2022, 4(2), 538-564; https://doi.org/10.3390/forecast4020030 - 11 Jun 2022
Cited by 1 | Viewed by 2211
Abstract
Because of the non-linearity inherent in energy commodity prices, traditional mono-scale smoothing methodologies cannot accommodate their unique properties. From this viewpoint, we propose an extended mode decomposition method useful for the time-frequency analysis, which can adapt to various non-stationarity signals relevant for enhancing [...] Read more.
Because of the non-linearity inherent in energy commodity prices, traditional mono-scale smoothing methodologies cannot accommodate their unique properties. From this viewpoint, we propose an extended mode decomposition method useful for the time-frequency analysis, which can adapt to various non-stationarity signals relevant for enhancing forecasting performance in the era of big data. To this extent, we employ variants of mode decomposition-based extreme learning machines namely: (i) Complete Ensemble Empirical Mode Decomposition with Adaptive Noise-based ELM Model (CEEMDAN-ELM), (ii) Ensemble Empirical Mode Decomposition-based ELM Model (EEMD-ELM) and (iii) Empirical Mode Decomposition Based ELM Model (EMD-ELM), which cut-across soft computing and artificial intelligence to analyze multi-commodity time series data by decomposing them into seven independent intrinsic modes and one residual with varying frequencies that depict some interesting characterization of price volatility. Our findings show that in terms of the model-specific forecast accuracy measures different dynamics in the two scenarios namely the (non) COVID periods. However, the introduction of a benchmark, namely the autoregressive integrated moving average model (ARIMA) reveals a slight change in the earlier dynamics, where ARIMA outperform our proposed models in the Japan gas and the US gas markets. To check the superiority of our models, we apply the model-confidence set (MCS) and the Kolmogorov-Smirnov Predictive Ability test (KSPA) with more preference for the former in a multi-commodity framework, which reveals that in the pre-COVID era, CEEMDAN-ELM shows persistence and superiority in accurately forecasting Crude oil, Japan gas, and US gas. Nonetheless, this paradigm changed during the COVID-era, where CEEMDAN-ELM favored Japan gas, US gas, and coal market with different rankings via the Model confidence set evaluation methods. Overall, our numerical experiment indicates that all decomposition-based extreme learning machines are superior to the benchmark model. Full article
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21 pages, 3610 KiB  
Article
Diffusion of Solar PV Energy in the UK: A Comparison of Sectoral Patterns
by Anita M. Bunea, Mariangela Guidolin, Piero Manfredi and Pompeo Della Posta
Forecasting 2022, 4(2), 456-476; https://doi.org/10.3390/forecast4020026 - 20 Apr 2022
Cited by 4 | Viewed by 2483
Abstract
The paper applies innovation diffusion models to study the adoption process of solar PV energy in the UK from 2010 to 2021 by comparing the trajectories between three main categories, residential, commercial, and utility, in terms of both the number of installations and [...] Read more.
The paper applies innovation diffusion models to study the adoption process of solar PV energy in the UK from 2010 to 2021 by comparing the trajectories between three main categories, residential, commercial, and utility, in terms of both the number of installations and installed capacity data. The effect of the UK incentives on adoptions by those categories is studied by analyzing the timing, intensity, and persistence of the perturbations on adoption curves. The analysis confirms previous findings on PV adoption, namely the fragile role of the media support to solar PV, the ability of the proposed model to capture both the general trend of adoptions and the effects induced by ad hoc incentives, and the dramatic dependence of solar PV from public incentives. Thanks to the granularity of the data, the results reveal several interesting aspects, related both to differences in adoption patterns depending on the category considered, and to some regularities across categories. A comparison between the models for number of installations and for installed capacity data suggests that the latter (usually more easily available than the former) may be highly informative and, in some cases, may provide a reliable description of true adoption data. Full article
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18 pages, 3241 KiB  
Article
Modelling Energy Transition in Germany: An Analysis through Ordinary Differential Equations and System Dynamics
by Andrea Savio, Luigi De Giovanni and Mariangela Guidolin
Forecasting 2022, 4(2), 438-455; https://doi.org/10.3390/forecast4020025 - 08 Apr 2022
Cited by 5 | Viewed by 3115
Abstract
This paper proposes the application of a multivariate diffusion model, based on ordinary differential equations, to investigate the energy transition in Germany. Specifically, the model is able to analyze the dynamic interdependencies between coal, gas and renewables in the energy market. A system [...] Read more.
This paper proposes the application of a multivariate diffusion model, based on ordinary differential equations, to investigate the energy transition in Germany. Specifically, the model is able to analyze the dynamic interdependencies between coal, gas and renewables in the energy market. A system dynamics representation of the model is also performed, allowing a deeper understanding of the system and the set-up of suitable strategic interventions through a simulation exercise. Such simulation provides a useful indication of how renewable energy consumption may be stimulated as a result of well-specified policies. Full article
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19 pages, 993 KiB  
Article
Side-Length-Independent Motif (SLIM): Motif Discovery and Volatility Analysis in Time Series—SAX, MDL and the Matrix Profile
by Eoin Cartwright, Martin Crane and Heather J. Ruskin
Forecasting 2022, 4(1), 219-237; https://doi.org/10.3390/forecast4010013 - 04 Feb 2022
Cited by 1 | Viewed by 2969
Abstract
As the availability of big data-sets becomes more widespread so the importance of motif (or repeated pattern) identification and analysis increases. To date, the majority of motif identification algorithms that permit flexibility of sub-sequence length do so over a given range, with the [...] Read more.
As the availability of big data-sets becomes more widespread so the importance of motif (or repeated pattern) identification and analysis increases. To date, the majority of motif identification algorithms that permit flexibility of sub-sequence length do so over a given range, with the restriction that both sides of an identified sub-sequence pair are of equal length. In this article, motivated by a better localised representation of variations in time series, a novel approach to the identification of motifs is discussed, which allows for some flexibility in side-length. The advantages of this flexibility include improved recognition of localised similar behaviour (manifested as motif shape) over varying timescales. As well as facilitating improved interpretation of localised volatility patterns and a visual comparison of relative volatility levels of series at a globalised level. The process described extends and modifies established techniques, namely SAX, MDL and the Matrix Profile, allowing advantageous properties of leading algorithms for data analysis and dimensionality reduction to be incorporated and future-proofed. Although this technique is potentially applicable to any time series analysis, the focus here is financial and energy sector applications where real-world examples examining S&P500 and Open Power System Data are also provided for illustration. Full article
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