Feature Papers of Forecasting 2021

Accurate forecasts of the number of newly infected people during an epidemic are critical for making effective timely decisions. This paper addresses this challenge using the SIMLR model, which incorporates machine learning (ML) into the epidemiological SIR model. For each region, SIMLR tracks the changes in the policies implemented at the government level, which it uses to estimate the time-varying parameters of an SIR model for forecasting the number of new infections one to four weeks in advance. It also forecasts the probability of changes in those government policies at each of these future times, which is essential for the longer-range forecasts. We applied SIMLR to data from in Canada and the United States, and show that its mean average percentage error is as good as state-of-the-art forecasting models, with the added advantage of being an interpretable model. We expect that this approach will be useful not only for forecasting COVID-19 infections, but also in predicting the evolution of other infectious diseases. Full article

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·s${}^{-1}$ and a correlation coefficient of 0.6. Full article

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

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 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

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

Thunderstorms are one of the most damaging weather phenomena in the United States, but they are also one of the least predictable. This unpredictable nature can make it especially challenging for emergency responders, infrastructure managers, and power utilities to be able to prepare and react to these types of events when they occur. Predictive analytical methods could be used to help power utilities adapt to these types of storms, but there are uncertainties inherent in the predictability of convective storms that pose a challenge to the accurate prediction of storm-related outages. Describing the strength and localized effects of thunderstorms remains a major technical challenge for meteorologists and weather modelers, and any predictive system for storm impacts will be limited by the quality of the data used to create it. We investigate how the quality of thunderstorm simulations affects power outage models by conducting a comparative analysis, using two different numerical weather prediction systems with different levels of data assimilation. We find that limitations in the weather simulations propagate into the outage model in specific and quantifiable ways, which has implications on how convective storms should be represented to these types of data-driven impact models in the future. Full article

Smoking causes substantial amount of mortality and morbidity. This article presents the findings from simulation models that projected the impact of five potential Tobacco Endgame strategies on smoking prevalence in Ontario by 2035 and expected impact of smoking prevalence “less than 5 by 35” on tax revenue. We used Ontario SimSmoke simulation for modelling the expected impact of four strategies: plain packaging, free cessation services, decreasing the number of tobacco outlets, and increasing tobacco taxes. Separate models were used to project the impact of increasing the minimum age to legally purchase tobacco to 21 years on smoking prevalence and impact of price and tax increase to achieve “less than 5 by 35” on taxation revenue. The combined effect of four strategies in Ontario SimSmoke Model are expected to reduce smoking prevalence by 8.5% in 2035. Increasing tobacco taxes had the greatest independent predicted decrease in smoking prevalence (2.8%) followed by raised minimum age for legal purchase to 21 years (2.4%), decreasing tobacco outlets (1.5%), free cessation services (0.7%), and plain packaging (0.6%). Increasing tobacco excise tax and prices are projected to have minimal impact on taxation revenue, with a decrease from 1.5 billion to 1.2 billion annual tax receipts. Full article

Energy efficiency topics have been covered by several energy management approaches in the literature, including participation in demand response programs where the consumers provide load reduction upon request or price signals. In such approaches, it is very important to know in advance the electricity consumption for the future to adequately perform the energy management. In the present paper, a load forecasting service designed for office buildings is implemented. In the building, using several available sensors, different learning parameters and structures are tested for artificial neural networks and the K-nearest neighbor algorithm. Deep focus is given to the individual period errors. In the case study, the forecasting of one week of electricity consumption is tested. It has been concluded that it is impossible to identify a single combination of learning parameters as different parts of the day have different consumption patterns. Full article

The intermittent and uncontrollable power output from the ever-increasing renewable energy sources, require large amounts of operating reserves to retain the system frequency within its nominal range. Based on day-ahead load forecasts, many research works have proposed conventional and stochastic approaches to define their optimum margins for reliability enhancement at reasonable production cost. In this work, we aim at delivering real-time load forecasting to lower the operating-reserve requirements based on intra-hour weather update predictors. Based on critical predictors and their historical data, we train an artificial model that is able to forecast the load ahead with great accuracy. This is a feed-forward neural network with two hidden layers, which performs real-time forecasts with the aid of a predictive model control developed to update the recommendations intra-hourly and, assessing their impact and its significance on the output target, it corrects the imposed deviations. Performing daily simulations for an annual time-horizon, we observe that significant improvements exist in terms of decreased operating reserve requirements to regulate the violated frequency. In fact, these improvements can exceed 80% during specific months of winter when compared with robust formulations in isolated power systems. Full article

Forecasting is a challenging task that typically requires making assumptions about the observed data but also the future conditions. Inevitably, any forecasting process will result in some degree of inaccuracy. The forecasting performance will further deteriorate as the uncertainty increases. In this article, we focus on univariate time series forecasting and we review five approaches that one can use to enhance the performance of standard extrapolation methods. Much has been written about the “wisdom of the crowds” and how collective opinions will outperform individual ones. We present the concept of the “wisdom of the data” and how data manipulation can result in information extraction which, in turn, translates to improved forecast accuracy by aggregating (combining) forecasts computed on different perspectives of the same data. We describe and discuss approaches that are based on the manipulation of local curvatures (theta method), temporal aggregation, bootstrapping, sub-seasonal and incomplete time series. We compare these approaches with regards to how they extract information from the data, their computational cost, and their performance. Full article