Forecast Updates Research Articles

On projection methods for functional time series forecasting

Two nonparametric methods are presented for forecasting functional time series (FTS). The FTS we observe is a curve at a discrete-time point. We address both one-step-ahead forecasting and dynamic updating. Dynamic updating is a forward prediction of the unobserved segment of the most recent curve. Among the two proposed methods, the first one is a straightforward adaptation to FTS of the k-nearest neighbors methods for univariate time series forecasting. The second one is based on a selection of curves, termed the curve envelope, that aims to be representative in shape and magnitude of the most recent functional observation, either a whole curve or the observed part of a partially observed curve. In a similar fashion to k-nearest neighbors and other projection methods successfully used for time series forecasting, we “project” the k-nearest neighbors and the curves in the envelope for forecasting. In doing so, we keep track of the next period evolution of the curves. The methods are applied to simulated data, daily electricity demand, and NOx emissions and provide competitive results with and often superior to several benchmark predictions. The approach offers a model-free alternative to statistical methods based on FTS modeling to study the cyclic or seasonal behavior of many FTS.

Put your money where your forecast is: Supply chain collaborative forecasting with cost-function-based prediction markets

Despite extensive research on the value of collaborative forecasting among supply chain partners, there has been limited attention to the actual mechanisms of forecast sharing. We propose using cost-function-based prediction markets as means of sharing demand forecasts. We consider a decentralized two-stage supply chain with a supplier who sells to a retailer, in which both partners receive demand forecast updates. In the first stage, the supplier and the retailer agree on a collaborative forecasting scheme to form a consensus forecast. We consider two schemes: collaborative forecasting using cost-function-based prediction markets in which partners pay a set-up cost to operate a prediction market, and direct collaborative forecasting in which partners directly share their forecasts with each other and resolve their forecast differences through costly actions. In the second stage, we analyze the game between the supplier and the retailer to determine the retailer’s optimal order quantity as well as the supplier’s optimal wholesale price. We show that demand forecast sharing using cost-function-based prediction markets results in a Pareto improvement of the partners’ profit over the alternative of directly sharing forecasts. Furthermore, we show that this Pareto improvement is monotone with respect to supply chain partners’ forecast accuracy. We conduct an extensive numerical study to provide further insights into the effect of the return on forecast sharing investment, forecast accuracy, and level of demand uncertainty on the benefits gained by implementing cost-function-based prediction markets.

Dynamic updating of post-earthquake damage and functional restoration forecasts of water distribution systems using Bayesian inferencing

A framework for dynamically updating post-earthquake functional recovery forecasts is presented to reduce the epistemic uncertainty in the predictive model. A Bayesian Network (BN) model is used to provide estimates of the total recovery time, and a process-based discrete event simulation (PBDES) model generates forecasts of the complete recovery trajectory. Both models rely on component damage and duration-based input parameters that are dynamically updated using Bayes’ theorem, as information becomes available throughout the recovery process. The effectiveness of the proposed framework is demonstrated through an application to the pipe network of the City of Napa water distribution system. More specifically, pipe damage and repair data from the 2014 earthquake are used as a point of comparison for the dynamic forecasts. It is shown that, over time, the mean value of the total recovery duration generated by the BN-based model converges to the observed value and the dispersion is reduced. Also, despite a crude initial estimate, the median trajectory generated by the PBDES model provides a reasonable approximation of the observed recovery within 30 days following the earthquake. The proposed framework can be used by emergency managers to investigate the efficacy of post-event mitigation measures (e.g. crew allocation, resource prioritization) utilizing the most current data and knowledge.

Sub-City Scale Hourly Air Quality Forecasting by Combining Models, Satellite Observations, and Ground Measurements.

While multiple information sources exist concerning surface‐level air pollution, no individual source simultaneously provides large‐scale spatial coverage, fine spatial and temporal resolution, and high accuracy. It is, therefore, necessary to integrate multiple data sources, using the strengths of each source to compensate for the weaknesses of others. In this study, we propose a method incorporating outputs of NASA’s GEOS Composition Forecasting model system with satellite information from the TROPOMI instrument and ground measurement data on surface concentrations. Although we use ground monitoring data from the Environmental Protection Agency network in the continental United States, the model and satellite data sources used have the potential to allow for global application. This method is demonstrated using surface measurements of nitrogen dioxide as a test case in regions surrounding five major US cities. The proposed method is assessed through cross‐validation against withheld ground monitoring sites. In these assessments, the proposed method demonstrates major improvements over two baseline approaches which use ground‐based measurements only. Results also indicate the potential for near‐term updating of forecasts based on recent ground measurements.

A General Model of Fractional Frequency Reuse: Modelling and Performance Analysis

Fractional Frequency Reuse (FFR) is a promising to improve the spectrum e ciency in the LongTerm Evolution (LTE) cellular network. In the literature, various research works have been conducted to evaluate the performance of FFR. However, the presented analytical approach only dealt with the special cases in which the users are divided into 2 groups and only two power levels are utilised. In this paper, we consider a general case of FFR in which the users are classified intogroups and each group is assigned a serving power level. The mathematical model of the general FFR is presented and analysed through a stochastic geometry approach. The derived analytical results in terms of average coverage probability can covered all the related well-known results in the literature.
 Keywords: 
 Fractional Frequency Reuse, LongTerm Evolution, coverage probability, stochastic geometry
 References
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Impact of generator start-up lead times on short-term scheduling with high shares of renewables

To cope with the variability and uncertainty introduced by, i.a., intermittent renewable energy sources, the flexible planning and operation of generation units is crucial. Their reaction time is constrained by the lead times on the start-up decisions, whereas the demand for flexibility and operating reserves depends on the market clearing frequency. The start-up lead times are limited by the operator’s tolerance for increased maintenance on the asset, which should be reflected in short-term scheduling models. To study this interaction between the market clearing frequency and the start-up capabilities of combined-cycle gas turbines, we develop a unit commitment model. The model considers multiple start-up trajectories and the scheduling decisions in joint energy-operating reserve and balancing markets. The uncertainty on wind power forecasts is presented via wind power forecast updates generated by a dedicated data-driven tool. Leveraging this model, we investigate the interaction between (i) the frequency of wind power forecast updates, linked to the market clearing frequency, (ii) cost-optimal operating reserve volumes and (iii) combined-cycle gas turbine start-up decisions. Results show that, in general, higher market clearing frequencies lead to lower operating costs, driven by decreasing volumes of operating reserves and facilitated by the fast start-up capabilities of combined-cycle gas turbines.

Revisiting the digital noise reduction in automatic contouring of “ice-water” objects

This work describes the practical implementation of the method for digital noise suppression during processing images containing ice information to recognize automatically the contours of «ice-water» objects during aerial photography. Images containing ice information have special characteristic structural features related to noise, e.g.granularity, glare, ice crumbs. This makes difficult or even impossible to recognize automatically the contours of ice-water objects. It is known that the success of the application of edge recognition methods depends on how much image noise is reduced. The paper discusses the construction method for the management of noise. The method is based on the sequential application of the Haar wavelet transform denoising using thresholding, clustering by k-means method. For the subsequent automatic construction of ice floes contours the Sobel operator is applied.The aim of the work is to develop a method capable to process digital images effectively that contain ice information with strong digital noise. In this work we treated the images of one-year ice containing strong digital image noise in the form of granularity and in the form of ice crumbs. A description of the features of each of the steps of the proposed method and practical application is given.As a result, the method was developed for processing images of ice information containing digital noise in absolute value commensurate with the basic data. It was noted that the use of the k-means method expands the scope. The k-rare method allows more detailed processing of ice information and distinguishes not only the contours of ice-water objects, but also the contours of ice crumbs.The conclusion formulates the main advantages of the method and the possible application of the algorithm in the process of local exploration of the ice conditions of the Northern Sea Route channel using unmanned aerial vehicle for aerial photography. The usage of unmanned aerial vehicle for aerial photography will increase the frequency of weather forecast updates and predict the appearance of ice objects at the ship’s heading. That will allow us to select the safest and most economical efficient route along the Northern Sea Route.The authors have no competing interests.

Development of Forecast Algorithm for Coronal Mass Ejection Speed and Arrival Time Based on Propagation Tracking by Interplanetary Scintillation g-Value

We have developed an algorithm for tracking coronal mass ejection (CME) propagation that allows us to estimate CME speed and its arrival time at Earth. The algorithm may be used either to forecast the CME’s arrival on the day of the forecast or to update the CME tracking information for the next day’s forecast. In our case study, we successfully tracked CME propagation using the algorithm based on g-values of interplanetary scintillation (IPS) observation provided by the Institute for Space- Earth Environmental Research (ISEE). We were able to forecast the arrival time (Δt = 0.30 h) and speed (Δv = 20 km/s) of a CME event on October 2, 2000. From the CME-interplanetary CME (ICME) pairs provided by Cane & Richardson (2003), we selected 50 events to evaluate the algorithm’s forecast capability. Average errors for arrival time and speed were 11.14 h and 310 km/s, respectively. Results demonstrated that g-values obtained continuously from any single station observation were able to be used as a proxy for CME speed. Therefore, our algorithm may give stable daily forecasts of CME position and speed during propagation in the region of 0.2–1 AU using the IPS g-values, even if IPS velocity observations are insufficient. We expect that this algorithm may be widely accepted for use in space weather forecasting in the near future.

Correcting the January optimism effect

AbstractEach month, various professional forecasters give forecasts for next year's real gross domestic product (GDP) growth and unemployment. January is a special month, when the forecast horizon moves to the following calendar year. Instead of deleting the January data when analyzing forecast updates, I propose a periodic version of a test regression for weak‐form efficiency. An application of this periodic model for many forecasts across a range of countries shows that in January GDP forecast updates are positive, whereas the forecast updates for unemployment are negative. I document that this January optimism about the new calendar year is detrimental to forecast accuracy. To empirically analyze Okun's law, I also propose a periodic test regression, and its application provides more support for this law.

Accurate response in agricultural supply chains

• An MMFE-based accurate response framework can improve a commercial seed manufacturer’s profitability significantly. • Implementation of the MMFE in practice can be challenging due to scarcity of historical data and the inconsistent number of forecast updates. • Seeds with low to moderate demand uncertainty should be processed in a few batches relatively early in the planning horizon. • Seeds with high demand uncertainty should be processed according to the multi-ordering strategy following forecast evolutions. In this paper, we introduce an accurate response framework in the context of commercial seed production by deploying the multiordering newsvendor model with dynamic forecast evolution to determine the timing and the quantity of production. We also demonstrate the challenges associated with applying the Martingale Model of Forecast Evolution (MMFE) to real data and propose practical remedies. More specifically, we fit the MMFE to the data for a variety of seeds (SKUs) produced by a major seed manufacturer and rank these SKUs based on their demand volume and volatility. We then assess the value of the MMFE-based accurate response by benchmarking it against the classic newsvendor model. We find that the MMFE-based accurate response can considerably increase the seed manufacturer’s profits by neatly dividing the product portfolio into four quadrants, according to demand volume and volatility, to determine the production timing and quantity. Such portfolio categorization would also enable the salesforce to better allocate their efforts to increase forecasting accuracy for the most critical products in their portfolio.

Characterizing Meteorological Forecast Impact on Microgrid Optimization Performance and Design

A microgrid consists of electrical generation sources, energy storage assets, loads, and the ability to function independently, or connect and share power with other electrical grids. Thefocus of this work is on the behavior of a microgrid, with both diesel generator and photovoltaic resources, whose heating or cooling loads are influenced by local meteorological conditions. Themicrogrid's fuel consumption and energy storage requirement were then examined as a function of the atmospheric conditions used by its energy management strategy (EMS). A fuel-optimal EMS, able to exploit meteorological forecasts, was developed and evaluated using a hybrid microgrid simulation. Weather forecast update periods ranged from 15 min to 24 h. Four representative meteorological sky classifications (clear, partly cloudy, overcast, or monsoon) were considered. Forall four sky classifications, fuel consumption and energy storage requirements increased linearly with the increasing weather forecast interval. Larger forecast intervals lead to degraded weather forecasts, requiring more frequent charging/discharging of the energy storage, increasing both the fuel consumption and energy storage design requirements. The significant contributions of this work include the optimal EMS and an approach for quantifying the meteorological forecast effects on fuel consumption and energy storage requirements on microgrid performance. The findings of this study indicate that the forecast interval used by the EMS affected both fuel consumption and energy storage requirements, and that the sensitivity of these effects depended on the 24-hour sky conditions.

Information rigidities and exchange rate expectations

Sluggish adjustment of expectations to new information is rational in an environment characterized by information costs and signal-to-noise problems. This paper investigates the role of such information rigidities for exchange rate expectations using data from Consensus Economics for ten emerging and industrial economies from 1999 until 2015. Our results confirm the importance of rigidities and show that the inclusion of forecast updates largely accounts for otherwise detected biases in expectation errors. Moreover, we find little evidence for a systematic effect of fundamentals or uncertainty measures on exchange rate disagreement.

Analysis of the impact of sub-hourly unit commitment on power system dynamics

This paper discusses the impact of the sub-hourly unit commitment problem on power system dynamics. Such an impact is evaluated by means of a co-simulation platform that embeds a sub-hourly stochastic mixed-integer linear programming security constrained unit commitment (sSCUC) into a time domain simulator, as well as includes a rolling planning horizon that accounts for forecast updates. The paper considers different sub-hourly sSCUC resolutions (i.e., 5 and 15 min) and different wind penetration levels (i.e., 25 and 50%). The focus is on the transient response of the system and on frequency variations following different sSCUC strategies, and different sSCUC wind power uncertainty and volatility. The case study consists of a comprehensive set of Monte Carlo simulations based on the 39-bus system.

Real-time correction method of Muskingum model based on Kalman filter

In flood forecasting, general flood forecasting models or empirical forecasts reflect the average optimal value or relationship curve under the previous data. However, in the operation forecast, the forecast plan value often deviates from the actual situation. This paper takes Muskingum model as an example, and uses the Kalman filter algorithm to correct the forecast results. The algorithm structure and principles were described detailed, and the numerical simulation test was set to verify the efficiency of the Kalman filter algorithm. The correct results with corrected method were compared. The results indicated that the efficiency of the updating system using Kalman filter algorithm was improved. Conclusively, the proposed method could be widely applied in real-time flood forecast updating.

Improved Dynamic System Response Curve Method for Real‐Time Flood Forecast Updating

AbstractThe dynamic system response curve (DSRC) method has been shown to effectively use error feedback correction to obtain updated areal estimates of mean rainfall and thereby improve the accuracy of real‐time flood forecasts. In this study, we address two main shortcomings of the existing method. First, ridge estimation is used to deal with ill‐conditioning of the normal equation coefficient matrix when the method is applied to small basins, or when the length of updating rainfall series is short. Second, the effects of spatial heterogeneity of rainfall on rainfall error estimates are accounted for using a simple index. The improved performance of the method is demonstrated using both synthetic and real data studies. For smaller basins with relatively homogeneous spatial distributions of rainfall, the use of ridge regression provides more accurate and robust results. For larger‐scale basins with significant spatial heterogeneity of rainfall, spatial rainfall error updating provides significant improvements. Overall, combining the two strategies results in the best performance for all cases, with the effects of ridge estimation and spatially distributed updating complementing each other.

Distributed model predictive control strategies for coordination of electro‐thermal devices in a cooperative energy management concept

SummaryThis work discusses three promising strategies for the compensation of deviations within the online phase of a cooperative energy management concept. Whereas in the planning phase, hourly schedules for the active electro‐thermal devices are negotiated on a daily basis, the second phase tracks the schedule, including forecast updates, and compensates the deviations. The latter online compensation is realized with a distributed and hierarchical‐distributed model predictive control strategies, which are compared with continuous rescheduling with receding horizon and evaluated in terms of computational and optimization performance and scalability. The optimization is implemented and deployed using an open‐source optimization framework, which automatically manages optimization control, integration of renewable generation and load forecasts, multi‐instantiation and communication over an internet of things communication protocol. We address the case of significant deviations from the day‐ahead forecast and the tracking of an obsolete schedule and suggest a combined method, which combines the advantages of rescheduling and a distributed MPC strategy. The method applies a dynamic threshold to evaluate the deviation trend over time and to decide if compensation is still feasible or if a rescheduling should be triggered. The methods are evaluated based on several performance indicators such as residual deviation, number of switching events and computation and communication requirements and scalability, and offer a methodology for the control design of systems of different dimensions.

Reactive production with preprocessing restriction in supply chains with forecast updates

We study a two-mode production system that allows the supply chain to utilize a second (reactive) production opportunity after demand information is updated before the selling season. Such reactive production quantity, however, is limited by the required preprocessing of raw materials that must be decided before the demand information is updated. We analyze the problem for two cases: perfect and imperfect demand information updates. For the case of imperfect demand updates, whether to engage in preprocessing that provides an opportunity for future reactive production depends on the relative magnitude of the resolved demand uncertainty compared with the unresolved one. In the case of perfect demand updates, however, it is independent of the demand characteristics. To the best of our knowledge, this is the first study to provide guidance on when and how much to invest in preprocessing in a situation with a general demand forecast updating. We also present a coordinating Pareto-improving reservation contract and show how the manufacturer can extract more profit by setting a lower reservation fee. Counterintuitively, we find that the manufacturer in the case of perfect demand updates benefits more from a contract without a return policy than that with a return policy. Numerical examples demonstrate that when the preprocessing restriction exists, the benefit of the two-mode production can be as large as 104% as compared to the single-mode production.

The order and transshipment decisions in a two-location inventory system with demand forecast updates

To improve profitability and reduce demand uncertainty, lateral transshipment is employed in this study to reallocate inventory with updated demand information. A decentralized inventory system with two retailers ordering from one common supplier is analyzed. Each retailer places the order independently before the selling season begins based on original demand forecast. As lead time elapses, more information is collected to update the demand forecast and transshipment is examined. This study constructs a two-stage model and applies backward induction to solve the problem. A threshold transshipment policy is proposed at stage 2. The existence of unique Nash equilibrium is proven at stage 1. Both the centralized system with transshipment and the newsvendor-like system without transshipment are studied as benchmarks to evaluate the performance of transshipment in the decentralized system. Extensive numerical examples are conducted to provide some valuable management implications. The results show that transshipment can lead to profit increase, especially in the system with high informative value of demand forecast updates.

The use of radial basis function and non-linear autoregressive exogenous neural networks to forecast multi-step ahead of time flood water level

Many different Artificial Neural Networks (ANN) models of flood have been developed for forecast updating. However, the model performance, and error prediction in which forecast outputs are adjusted directly based on models calibrated to the time series of differences between observed and forecast values, are very interesting and challenging task. This paper presents an improved lead time flood forecasting using Non-linear Auto Regressive Exogenous Neural Network (NARXNN), which shows better performance in term of forecast precision and produces minimum error compared to neural network method using Radial Basis Function (RBF) in examined 12-hour ahead of time. First, RBF forecasting model was employed to predict the flood water level of Kelantan River at Kuala Krai, Kelantan, Malaysia. The model is tested for 1-hour and 7-hour ahead of time water level at flood location. The same analysis has also been taken by NARXNN method. Then, a non-linear neural network model with exogenous input promoted with enhancing a forecast lead time to 12-hour. Both about the performance comparison has briefly been analyzed. The result verified the precision of error prediction of the presented flood forecasting model.

Multi-Agent-Based Forecast Update Methods for Profit Enhancement of Intermittent Distributed Generators in a Smart Microgrid

Uncertainty in generation from intermittent sources makes a strong case to have effective forecasting methods. However, errors in forecast lead to losses to the distributed generation (DG) owners. In this article, multi-agent-based forecast update methods are proposed which minimize the forecast errors. The effectiveness of the proposed methods in enhancing the profit of intermittent generators and microgrid operational cost is analyzed using a microgrid with two scenarios, namely simple ownership and multiple ownership. A modified IEEE 13 bus system is used as the case study system and the system simulation for the microgrid is performed on the OpenDSS platform and the proposed multi-agent system is developed using JAVA Agent DEvelopment (JADE) framework. From the simulation results, the proposed approaches are found effective in increasing profit margins for the investors or owners of the DGs.