Short-term Forecasting Method Research Articles

Short-Term Photovoltaic Power Probabilistic Forecasting Based on Temporal Decomposition and Vine Copula

With the large-scale development of solar power generation, highly uncertain photovoltaic (PV) power output has an increasing impact on distribution networks. PV power generation has complex correlations with various weather factors, while the time series embodies multiple temporal characteristics. To more accurately quantify the uncertainty of PV power generation, this paper proposes a short-term PV power probabilistic forecasting method based on the combination of decomposition prediction and multidimensional variable dependency modeling. First, a seasonal and trend decomposition using a Loess (STL)-based PV time series feature decomposition model is constructed to obtain periodic, trend, and residual components representing different characteristics. For different components, this paper develops a periodic component prediction model based on TimeMixer for multi-scale temporal feature mixing, a long short-term memory (LSTM)-based trend component extraction and prediction model, and a multidimensional PV residual probability density prediction model optimized by Vine Copula optimized with Q-Learning. These components’ results form a short-term PV probabilistic forecasting method that considers both temporal features and multidimensional variable correlations. Experimentation with data from the Desert Knowledge Australia Solar Center (DKASC) demonstrates that the proposed method reduced root mean square error (RMSE) and mean absolute percentage error (MAPE) by at least 14.8% and 22%, respectively, compared to recent benchmark models. In probability interval prediction, while improving accuracy by 4% at a 95% confidence interval, the interval width decreased by 19%. The results show that the proposed approach has stronger adaptability and higher accuracy, which can provide more valuable references for power grid planning and decision support.

Spatiotemporal variations of precipitation patterns in the middle and lower reaches of Yangtze River Basin

Context With escalating global climate change, regional flood disasters have become increasingly prevalent. Precipitation, as a primary influencing factor, has garnered significant attention. Aims This study is based on precipitation data to investigate the spatiotemporal characteristics of precipitation in the middle and lower reaches of Yangtze River Basin (MLYB), trying to explore more concise methods for precipitation forecasting. Methods Statistical methods were employed to analyse historical precipitation patterns, followed by forecasting future trends using statistical time series models. Key results Precipitation in the MLYB exhibited a decreasing trend during 1961–2010, which shifted to an increasing trend after 2011, becoming more pronounced since 2017. Precipitation patterns in the MLYB were clearly increasing in the east and decreasing in the west, with the Taihu Basin showing the greatest rise. The ARIMA model predicted a significant increase in precipitation after 2022. Conclusions In recent years, precipitation in the MLYB has significantly increased, especially in downstream areas. Although the ARIMA model offers an effective and reasonably simple method for short-term forecast, it struggles with complex terrain influences. Implications These findings provide a theoretical basis for flood prevention in the MLYB, as well as a reference for precipitation prediction simulations in data-limited regions.

A short-term electricity load forecasting method integrating empirical modal decomposition with SAM-LSTM

Short-term power load forecasting is the basis for ensuring the safe and stable operation of the power system. However, because power load forecasting is affected by weather, economy, geography, and other factors, it has strong instability and nonlinearity, making it difficult to improve the accuracy of short-term power load forecasting. To solve the above problems, a load forecasting method combining empirical modal decomposition (EMD) and long short-term memory neural network (LSTM) has been proposed. The original signal is first decomposed into a series of eigenmode functions and a residual quantity using the EMD algorithm. Subsequently, all the components are fed into the LSTM network. To further improve the load prediction accuracy, a self-attention mechanism is introduced for large component signals to further explore the internal correlation of the data, and the Sparrow Optimisation Algorithm (SSA) is used to optimize the LSTM hyperparameters. Combining EMD, LSTM, self-attention mechanism (SAM), and SSA, the EMD-SSA- SAM -LSTM method for short-term power load forecasting is further proposed. The results show that the coefficient of determination (R2) of the method is 0.98, the mean absolute error (MAE) is 0.013, the root mean square error (RMSE) is 0.018, and the mean absolute percentage error (MAPE) is 2.57%, which verifies that the proposed model can improve the accuracy of load forecasting, and has a certain application prospect.

Long-Term Regional Influenza-Like-Illness Forecasting Using Exogenous Data.

Disease forecasting is a longstanding problem for the research community, which aims at informing and improving decisions with the best available evidence. Specifically, the interest in respiratory disease forecasting has dramatically increased since the beginning of the coronavirus pandemic, rendering the accurate prediction of influenza-like-illness (ILI) a critical task. Although methods for short-term ILI forecasting and nowcasting have achieved good accuracy, their performance worsens at long-term ILI forecasts. Machine learning models have outperformed conventional forecasting approaches enabling to utilize diverse exogenous data sources, such as social media, internet users' search query logs, and climate data. However, the most recent deep learning ILI forecasting models use only historical occurrence data achieving state-of-the-art results. Inspired by recent deep neural network architectures in time series forecasting, this work proposes the Regional Influenza-Like-Illness Forecasting (ReILIF) method for regional long-term ILI prediction. The proposed architecture takes advantage of diverse exogenous data, that are, meteorological and population data, introducing an efficient intermediate fusion mechanism to combine the different types of information with the aim to capture the variations of ILI from various views. The efficacy of the proposed approach compared to state-of-the-art ILI forecasting methods is confirmed by an extensive experimental study following standard evaluation measures.

Short-term load forecasting of regional integrated energy system based on spatio-temporal convolutional graph neural network

Accurate short-term load forecasting has significant reference value for the preliminary planning and subsequent coordinated control of regional integrated energy systems. However, main limitations are: (1) Insufficient exploration of the complex correlations among diverse loads and between loads and meteorological factors; and (2) Feature information extraction only from the time dimension, neglecting the spatial dimension, resulting in incomplete feature information extraction. This paper proposes a new method for short-term load forecasting, namely the Attention Spatial–temporal Graph Convolutional Network (ATSTGCN) method. Firstly, the Grey Relational Analysis (GRA) method is used to analyze the correlation between diverse loads, meteorological features, and load features, constructing the input data for the graph neural network. Then, synchronous graph convolution is introduced to extract information between features. Next, the spatio-temporal attention mechanism is incorporated into the synchronous graph convolution to capture spatio-temporal features and reduce information loss using a spatio-temporal window. Finally, a short-term load forecasting method based on ATSTGCN for regional integrated energy systems is constructed. Extensive simulation experiments are conducted on the publicly available dataset from Arizona State University’s online system. The experimental results show that compared to other methods, the proposed method improves MAPE, MAE, and RMSE by 5.6%, 11.7%, and 11.6%.

Short-term forecasting method for prevailing orientation of leads in the Laptev Sea ice cover during winter-spring season

Sea ice leads are potential routes of easier navigation in the Arctic seas during winter-spring season. Forecasts of the prevailing orientation of ice leads are required for the early selection of the optimal route of a ship in ice. An automated method for short-term forecasting of the prevailing orientation of leads in the Laptev Sea is developed in AARI based on the analog method. Data sources for making a forecast include predictive fields of atmospheric pressure up to 3 days in advance and historical data on surface atmospheric pressure, ice drift and sea ice leads. The data on ice leads are obtained by interpreting SuomiNPP satellite images in a visible and infrared range at 375 m spatial resolution for the cold seasons 2016–2022. The ice drift data are freely available OSI SAF data. In order to make a forecast of the prevailing orientation of leads by a predictive atmospheric pressure field, it is necessary to select from an electronic data archive a historical field of atmospheric pressure that is the most similar to the predictive field. The lead characteristics on the selected date are taken as the predictive ones. Automated selection of an analog consists in searching for an atmospheric pressure field from the electronic data archive which has minimal metrics for the forecast date together with 2 previous days. The metric is calculated as the sum of the squared differences between the change in atmospheric pressure per 1 km in the latitudinal and meridional directions between the predictive atmospheric pressure field and the potential analog field. Actual lead data for a date of the selected analog may not be available due to thick clouds. In this case, the direction of the minor axis of the strain ellipse calculated from ice drift data is taken as the prevailing orientation of leads. The success ratio of diagnostic forecasts by the method developed is 72 %. Comparison of the success ratio of the diagnostic forecasts with the results of inertial and climatic forecasts demonstrates a high effectiveness of the method for cases of abrupt change in the direction of air flows over the Laptev Sea within the first 1–2 days.

Improving the Method of Short-term Forecasting of Electric Load in Distribution Networks using Wavelet transform combined with Ridgelet Neural Network Optimized by Self-adapted Kho-Kho Optimization Algorithm

This paper proposes a new method for short-term electric load forecasting using a Ridgelet Neural Network (RNN) combined with a wavelet transform and optimized by a Self-Adapted (SA) Kho-Kho algorithm (SAKhoKho). The aim of this method is to improve the accuracy and reliability of electric load forecasting, which is essential for the planning and operation of competitive electrical networks. The proposed method uses the Wavelet Transform (WT) to decompose the load data into different frequency components and applies the RNN to each component separately. The RNN is, then, optimized by the SAKhoKho algorithm, which is an improved version of the KhoKho algorithm that can adapt the search parameters dynamically. The proposed method is trained and tested on the Zone Preliminary Billing Data from the PJM regulatory area, which is updated every two weeks based on the Intercontinental Exchange (ICE) figures. The proposed method is compared with six other cutting-edge methods from the literature, including SVM/SA, hybrid, ARIMA, MLP/PSO, CNN, and RNN/KhoKho/WT. The results show that the proposed method achieves the lowest Mean Absolute Error (MAE) of 7.7704 and Root Mean Square Error (RMSE) of 17.4132 among all the methods, indicating its superior performance. The proposed method can capture the temporal dependencies in the load data and optimize the RNN's weights to minimize the error function. The proposed method is a promising technique for electric load forecasting, as it can provide accurate and reliable predictions for the next hour based on the previous 24 h of data.

Hydrological Verification of Two Rainfall Short-Term Forecasting Methods with Floods Anticipation Perspective

Abstract Flood forecasting remains a significant challenge, particularly when dealing with basins characterized by small drainage areas (i.e., 103 km2 or lower with response time in the range 0.5–10 h) especially because of the rainfall prediction uncertainties. This study aims to investigate the performances of streamflow predictions using two short-term rainfall forecast methods. These methods utilize a combination of a nowcasting extrapolation algorithm and numerical weather predictions by employing a three-dimensional variational assimilation system, and nudging assimilation techniques, meteorological radar, and lightning data that are frequently updated, allowing new forecasts with high temporal frequency (i.e., 1–3 h). A distributed hydrological model is used to convert rainfall forecasts into streamflow prediction. The potential of assimilating radar and lightning data, or radar data alone, is also discussed. A hindcast experiment on two rainy periods in the northwest region of Italy was designed. The selected skill scores were analyzed to assess their degradation with increasing lead time, and the results were further aggregated based on basin dimensions to investigate the catchment integration effect. The findings indicate that both rainfall forecast methods yield good performance, with neither definitively outperforming the other. Furthermore, the results demonstrate that, on average, assimilating both radar and lightning data enhances the performance.

An ensemble-based approach for short-term load forecasting for buildings with high proportion of renewable energy sources

The increasing integration of renewable energy sources into large buildings and structures has emphasized the importance of effective control systems to optimize resource use, grid stability, and reliability. In this context, load forecasting is critical for predicting future energy demand and managing the intermittency and variability of renewable energy resources to ensure a stable performance. This study proposes an ensemble-based short-term load forecasting (STLF) method tailored for buildings. It combines parallel and series approaches for a 24-hour forecasting horizon. The parallel approach uses similar historical days to capture the general load trend, holidays, and user behavior, while the series component employs a deep learning-based neural network to address short-term changes. The final ensemble-based forecast is the culmination of these two methods. Evaluated using real load profile data sets with 15-minute resolution, the proposed method demonstrates more than 23% and 24% improvements in MAE and RMSE, respectively, compared to other methods. These results demonstrated the superior performance of the proposed method in terms of both accuracy and robustness, making it an effective solution for short-term load forecasting in buildings heavily reliant on renewable energy.

Adaptive temporal transformer method for short-term wind power forecasting considering shift in time series distribution

In wind power prediction, the input probability distributions in the different sub-periods are shifted owing to the strong randomness of the input features, such as wind speed and direction. This may violate the assumption for machine learning that the training and test data meet the condition of being independent and identically distributed, resulting in an insufficient generalization ability of the prediction model that is trained with the training data and applied to unknown test data. To address this problem, this study proposes an adaptive temporal transformer method for short-term wind power forecasting. First, a temporal transformer model with a gate recurrent unit and multi-head attention layers was used to extract the short- and long-term temporal information of the multiple input variables. Then, an adaptive learning strategy consisting of two stages—temporal distribution characterization and temporal distribution matching—was developed to explore the common knowledge hidden in each sub-period. The case results for an actual wind farm in northwest China showed that the proposed method could effectively weaken the adverse effects of the shifts in time series distribution on forecasting and improve the accuracy and generalization of short-term wind power prediction.

Short-term electricity load forecasting based on CEEMDAN-FE-BiGRU-Attention model

Abstract Intending to address the volatility and intricacy of power load data, a novel method for short-term power load forecasting is presented, utilizing the CEEMDAN-FE-BiGRU-Attention model. This paper introduces the innovative adaptive noise fully integrated Empirical Mode decomposition (CEEMDAN) algorithm, which effectively decomposes the sequence into modal components of various frequencies and residual components. This decomposition serves to reduce the complexity inherent in the original time series. Subsequently, the fuzzy entropy (FE) algorithm is employed to calculate the time complexity of each component, allowing for the reconstruction of different scale sequences, thereby enhancing computational efficiency. Finally, the sequences of varying scales are input into the bidirectional recurrent neural network (BiGRU) model, incorporating the attention mechanism for prediction. Notably, the prediction accuracy of the BiGRU model with attention mechanism surpasses that of the baseline GRU model for multifeature time series, resulting in a significant improvement in prediction accuracy. Experimental findings demonstrate that the proposed model outperforms traditional approaches, enabling better capture of the variation trends in power load data while reducing time series complexity. Moreover, the proposed model exhibits a remarkable reduction in mean absolute percentage error and root mean square error values by 90.24% and 85.05%, respectively, when compared to the single BiGRU-Attention model. This enhancement enhances the accuracy of power load prediction. These innovative methodologies endow the power load forecasting method proposed in this paper with greater potential and viability for real-world applications.

An Efficient Framework for Short-Term Electricity Price Forecasting in Deregulated Power Market

It is widely acknowledged that electricity price forecasting become an essential factor in operational activities, planning, and scheduling for the participant in the price-setting market, nowadays. Nevertheless, electricity price became a complex signal due to its non-stationary, non-linearity, and time-variant behavior. Consequently, a variety of artificial intelligence techniques are proposed to provide an efficient method for short-term electricity price forecasting. BSA as the recent augmentation of optimization technique, yield the potential of searching a closed-form solution in mathematical modeling with a higher probability, obviating the necessity to comprehend the correlations between variables. Concurrently, this study also developed a feature selection technique, to select the input variables subsets that have a substantial implication on forecasting of electricity price, based on a combination of mutual information (MI) and SVM. For the verification of simulation results, actual data sets from the Ontario energy market in the year 2020 covering various weather seasons are acquired. Finally, the obtained results demonstrate the feasibility of the proposed strategy through improved preciseness in comparison with the distinctive methods.

ВКЛАД НГМС РК В СЕЛЕВУЮ БЕЗОПАСНОСТЬ СТРАНЫ

In this paper, the scientific activities of the National Hydrometeorological Service of Kazakhstan to study the origin, development and degradation of debris flows, carried out with the aim of reducing the damage caused by debris flows, are presented over a long period. The features of precipitation, emptying of surface and intraglacial reservoirs, mineralogical and granulometric compositions of debris rocks, geomorphological conditions of debris flow formation, recurrence and characteristics of debris flows were studied. Methods for computation of the viscosity and plasticity of debris-flow mass, used in mathematical modeling of debris flow formation have been developed. A method for ultra-short-term forecasting of catastrophic debris flows caused by rainfall, based on data on preliminary soil moisture, the position of the zero isotherm and seasonal snow line, and current actual meteorological information has been developed. The feasibility of this method is tens of times greater than the feasibility of the method for short-term forecasting of debris flows caused by rainfall, which was used previously. The computation of the characteristics of debris flows is based on the discovery of an ambiguous dependence between the density of debris-flow mass and the slope of way of debris flow. A debris-flow hazard map for the territory of the Republic of Kazakhstan has been developed. A concept for protection against debris flows of Almaty city under climate changing has been proposed. A geological and geomorphological method has been developed for determining the genesis of fan, which allows optimizing the work on assessing the risk of urbanization of territories located in the mountain and foothill zones.

A Hybrid Deep Learning Method for Short-Term Traffic Flow Forecasting: GSA-LSTM

A Hybrid Deep Learning Method for Short-Term Traffic Flow Forecasting: GSA-LSTM

Testing novel methods for short-term forecasting of European glass eel recruitment

Recruitment forecasting constitutes a major issue in population dynamics, especially in stock assessments. In many cases, high recruitment stochasticity thwarts the determination of a stock-recruitment (SR) relationship. When no SR relationship is available, recruitments can be projected from past trends using methods such as the geometric means of the past recruitments. This approach implicitly assumes stability in upcoming years and might not be appropriate in the case of regime shifts. Recruitment forecasting is particularly critical when predicting fishing opportunities for recruitment fisheries. The fishery of glass European eels (Anguilla anguilla) is an example of a recruitment fishery. The French glass eel fishery, which is currently guided by two models to forecast upcoming recruitments and derive fishing opportunities, has failed to achieve management targets. This study develops new models that forecast glass eel recruitments without assumptions about the SR relationship, and with flexible assumptions about future recruitment evolutions. Tests based on multiple criteria found the best performance in a random slope model, which provides flexibility to track recruitment dynamics while balancing the interests of fisheries, management and conservation. This model has wide potential for trend projections in natural resource management, economics and other fields.

A Method of Short-Term Forecasting of Ultradian and Infradian Rhythms Based on the Recording of Fluctuations in the Rates of Radioactive Decay of Potassium-40

A Method of Short-Term Forecasting of Ultradian and Infradian Rhythms Based on the Recording of Fluctuations in the Rates of Radioactive Decay of Potassium-40

Short-Term Dynamic Ampacity Forecasting for the Fully Insulated Busbar Taped Joint

The prior research on dynamic thermal rating have not taken into account the joint that could potentially form a hot spot in the power system. This paper proposes a novel method for short-term dynamic ampacity forecasting of a fully insulated busbar taped joint. The first-order thermal network, in which the thermal parameters are identified through FEM thermal analysis, simplifies 3-D temperature field of the joint. The surface temperature of the busbar is used to extract environmental information based on the equivalence principle of boundary conditions. Time series modelling is employed to forecast short-term environmental changes over the course of the following hour. On this basis, a rolling forecasting of the dynamic ampacity can be realized. A dynamic thermal rating test was carried out to validate and improve the model. The measured joint conductor temperature is nearly but consistently below the temperature limit of 90°C during the whole test. This indicates that the dynamic thermal rating is successfully accomplished in the laboratory using the proposed algorithm, increasing the transmission capacity of the insulated busbar.

Forecasting water discharges of the river Marmarik (Armenia) according to the kinematic wave equation

The article provides an overview of methods for short-term forecasting of water flow in mountain rivers based on physical-statistical and regression equations, conceptual approaches. Requirements for forecast models for the Armenian rivers are noted, which correspond to the kinematic wave model, but so far there are no forecast results for the Armenian rivers using this model. The purpose of the study was to test a mathematical model of a kinematic wave for a short-term forecast of water discharges on the mountainous Armenian river Marmarik. The model of a kinematic wave can be obtained after simplifying
 the model of channel runoff in a general form, i.e., after excluding from consideration deformations, local constant and variable backwaters from narrowing of channels, confluence of rivers, surge phenomena, and also after excluding the influence of the lower boundary condition, which is responsible for the characteristic of the diffusion spreading of the flood wave.
 A kinematic wave model was tested on the Marmarik River, which is an important water body in Armenia. We used series of observations of urgent water discharges during the spring flood from April 1 to June 30 for 2015—2018 at the top of the river Marmarik — Hankavan settlement, lower alignment — Aghavnadzor settlement and at the alignment on the main tributary of the river Gomraget — Meghradzor village. It was found that in the dynamic parametrization of the model, the anthropogenic impact should be introduced as an additional parameter. The obtained values of water discharges when making verification forecasts in the aggregate characterize the kinematic wave model as an effective approach according to
 the criteria for assessing the quality of forecast methods of the Hydrometeorological Center and Nash—Sutcliffe. The kinematic wave model is easy to use with a small amount of initial information without meteorological data, which will facilitate its implementation for practical applications.

MATHEMATICAL METHODS OF ECONOMIC ANALYSIS SOLVING PROBLEMS

MATHEMATICAL METHODS OF ECONOMIC ANALYSIS SOLVING PROBLEMS

Learning from the past: A short term forecast method for the COVID-19 incidence curve.

The COVID-19 pandemy has created a radically new situation where most countries provide raw measurements of their daily incidence and disclose them in real time. This enables new machine learning forecast strategies where the prediction might no longer be based just on the past values of the current incidence curve, but could take advantage of observations in many countries. We present such a simple global machine learning procedure using all past daily incidence trend curves. Each of the 27,418 COVID-19 incidence trend curves in our database contains the values of 56 consecutive days extracted from observed incidence curves across 61 world regions and countries. Given a current incidence trend curve observed over the past four weeks, its forecast in the next four weeks is computed by matching it with the first four weeks of all samples, and ranking them by their similarity to the query curve. Then the 28 days forecast is obtained by a statistical estimation combining the values of the 28 last observed days in those similar samples. Using comparison performed by the European Covid-19 Forecast Hub with the current state of the art forecast methods, we verify that the proposed global learning method, EpiLearn, compares favorably to methods forecasting from a single past curve.