Short-term Forecasting Model Research Articles

КРАТКОСРОЧНОЕ ПРОГНОЗИРОВАНИЕ ВЫРАБОТКИ МОЩНОСТИ ВЕТРОЭНЕРГЕТИЧЕСКИХ УСТАНОВОК НА БАЗЕ АНСАМБЛЕВЫХ МЕТОДОВ МАШИННОГО ОБУЧЕНИЯ

Regression models for short-term forecasting of output capacities of wind power plants operating as part of wind farms using machine learning algorithms are considered

Short-term Load Forecasting with Deep Learning Techniques

Short-term load forecasting is essential to the modern power system, which is the basis to guarantee the equilibrium of power supply and demand. It not only ensures the secure and stable operation of power grid, but also provides an important basis for power load dispatch, which can effectively reduce the economic loss caused by energy surplus or energy shortage in a short term. In the previous research work, traditional linear regression models, typical machine learning models and other models have been explored, but there are still problems of low accuracy or single influence factors. In this paper, a short-term forecasting model based on Temporal Convolutional Network (TCN) is proposed. Historical power load data, weather conditions, date information and their combinations are separately used to forecast the power load in different prediction models. Compared with other models, the evaluation metric of TCN model shows that its prediction accuracy is higher, and it is more suitable for short-term load forecasting.

Short-term power load forecasting for combined heat and power using CNN-LSTM enhanced by attention mechanism

This study proposes a new approach for short-term power load forecasting using a combination of convolutional neural networks (CNN), long short-term memory (LSTM), and attention mechanisms to address the issue of information loss due to excessively long input time series data. The objective is to enhance the accuracy of short-term power load prediction, which is crucial for efficient energy management. The study analyzes the relationship between the target load and the collected parameters, identifying the most influential factors using Pearson correlation coefficient analysis. A one-dimensional CNN layer is utilized to extract high-dimensional features from the input data, followed by an LSTM layer that captures temporal correlations within the historical sequences. Finally, an attention mechanism is introduced to optimize the weight of the LSTM output, enhance the influence of key information, and optimize the overall prediction model. The performance of the proposed model is evaluated using two benchmark models based on mean absolute percentage error (MAPE), root mean square error (RMSE), and mean absolute error (MAE) metrics. The results show that the CNN-LSTM-A model outperforms the traditional LSTM model regarding power load prediction accuracy for two thermal power units, with an improvement of 7.3% and 5.7%, respectively, indicating superior performance. Therefore, this study demonstrates the effectiveness of the proposed CNN-LSTM-A model for short-term power load forecasting, which has potential applications in the energy industry. In conclusion, the proposed approach can improve the accuracy of power load forecasting, leading to more efficient energy management and cost savings. Additionally, the study highlights the importance of incorporating attention mechanisms into traditional LSTM models for power load forecasting, as it helps to optimize the weight of the LSTM output and improve the accuracy of the predictions. The proposed CNN-LSTM-A model can be potentially useful for energy companies and policymakers in making informed decisions regarding energy production and consumption. Overall, this study provides a valuable contribution to power load forecasting, and the proposed approach could be extended to other areas of time-series forecasting in the future.

Research on short-term forecasting method of photovoltaic power generation based on clustering SO-GRU method

In order to enhance the stability of the power grid and increase the ability of the power grid to absorb photovoltaic energy, while enriching the prediction model of photovoltaic power generation, improve the precision reading of the prediction model. This paper proposes a short-term forecasting model of photovoltaic power generation based on K-means++clustering- Snake Optimizer algorithm (SO) - Gate Recurrent Unit(GRU). First, Pearson correlation coefficient analysis is used to screen out variables that are not related to the actual output of power generation.Then, select historical data with similar weather fluctuation degree as the training sample through K-means++clustering, use SO algorithm to optimize the parameters of GRU network, and use the optimized GRU network to predict photovoltaic power generation, Finally, through the simulation of a photovoltaic power station in Beijing, the results show that the prediction model proposed in this paper has improved the prediction accuracy compared with other traditional prediction models when the weather is almost no fluctuation, the weather has some fluctuations and the weather fluctuation is very severe. This research can provide reference for energy storage research of integrated energy system.

Research on short-term load forecasting of power system based on IWOA-KELM

A short-term power load forecasting (STPLF) model based on the Improved Whale Optimization Algorithm (IWOA) optimized Kernel Extreme Learning Machine (KELM) is proposed to address the problems of high randomness and low forecasting accuracy of electricity loads. The KELM model is constructed, and the IWOA is used to optimize the core and penalty parameters of the KELM to establish the IWOA-KELM electricity load forecasting model. Combined with the actual data of a certain region, the forecasting analysis results show that the convergence speed and forecasting accuracy of the method are greatly improved compared with IWOA-BP, IWOA-SVM and IWOA-ELM forecasting methods.

A hybrid model for online short-term tidal energy forecasting

A hybrid model is proposed for the short-term online prediction of tidal currents. The harmonic residual analysis (HRA) model is designed to augment the numerical schemes employed by tidal energy installations by forecasting the residual error of existing methods. Using a combination of techniques from Information and Fractal Theory, a novel component selection criterion for singular spectrum analysis (SSA) is used to remove true noise from the residual time series and to decompose the signal into components that are appropriate for linear-recurrent forecasting (LRF) and high order fuzzy time series (HOFTS) respectively. The performance of the HRA method is evaluated using a combination of simulated and real data from sites in the United Kingdom and the United States. Results demonstrate the model’s viability for 6-minute and 1-hour forecast horizons across sites exhibiting variable degrees of non-linearity. Empirical analysis of the resultant tidal energy forecast verifies the superior accuracy and reliability of the HRA method when compared with existing numerical schemes. Simulated data from three sites at the Pentland Firth, UK is also provided to facilitate further study of the site’s power generation characteristics and to allow for direct model performance comparisons.

Performance evaluation of artificial neural network model in hybrids with various preprocessors for river streamflow forecasting

Abstract Accurate forecasting of hydrological processes and sustainable management of water resources is inevitable, especially for flood control and water resource shortage crisis in low-water areas with an arid and semi-arid climate, which is a limitation for residents and various structures. The present study uses different data preprocessing techniques to deal with complex data and extract hidden features from the stream time series. In the next step, the decomposed time series were used, as input data, to the artificial neural network (ANN) model for streamflow modeling and forecasting. The preprocessors employed included discrete wavelet transform (DWT), empirical mode decomposition (EMD), complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), successive variational mode decomposition (SVMD), and multi-filter of the smoothing (MFS). These preprocessors were used in hybrid with the ANN model to forecast the daily streamflow. In general, the results showed that the optimal performance of hybrid models has two basic steps. The first step is choosing a suitable approach to utilizing the input data to the model. The second step is to use the appropriate preprocessor. Overall, the results show that the MFS-ANN model in short-term forecasting and the SVMD-ANN model in long-term forecasting performed better than other hybrid models.

Reconstruction of Unsteady Wind Field Based on CFD and Reduced-Order Model

Short-term wind power forecasting is crucial for updating the wind power trading strategy, equipment protection and control regulation. To solve the difficulty surrounding the instability of the statistical model and the time-consuming nature of the physical model in short-term wind power forecasting, two innovative wind field reconstruction methods combining CFD and a reduced-order model were developed. In this study, POD and Tucker decomposition were employed to obtain the spatial–temporal information correlation of 2D and 3D wind fields, and their inverse processes were combined with sparse sensing to reconstruct multi-dimensional unsteady wind fields. Simulation and detailed discussion were performed to verify the practicability of the proposed algorithms. The simulation results indicate that the wind speed distributions could be reconstructed with reasonably high accuracy (where the absolute velocity relative error was less than 0.8%) using 20 sensors (which only accounted for 0.04% of the total data in the 3D wind field) based on the proposed algorithms. The factors influencing the results of reconstruction were systematically analyzed, including all-time steps, the number of basis vectors and 4-mode dimensions, the diversity of CFD databases, and the reconstruction time. The results indicated that the reconstruction time could be shortened to the time interval of data acquisition to synchronize data acquisition with wind field reconstruction, which is of great significance in the reconstruction of unsteady wind fields. Although there are still many studies to be carried out to achieve short-term predictions, both unsteady reconstruction methods proposed in this paper enable a new direction for short-term wind field prediction.

Short-term and seasonal time series models for online marketing campaigns

Marketing companies use the market response to price products, determine advertising expenditures, forecast sales or prepare and test the effectiveness of various marketing plans and campaigns. Predictions of future traffic for online marketing campaigns can be based on data analysis and market response models. Mathematical models have become the main tools for marketing decision-making. The main goal of this paper is to describe and show how to use behavioral modelling of potential customers in online marketing campaigns. In addition to the basic ARMA model for short-term website traffic forecasting, we evaluate the TBATS and Prophet models. Both models comprehensively capture seasonal and holiday fluctuations. More specifically we show how time series modelling can be incorporated into the evaluation of online marketing campaign traffic forecasts for marketing agency clients.

Agricultural products price prediction based on improved RBF neural network model

ABSTRACT The agricultural products price has been affecting people’s livelihood issues and economic and social security and stability. The cyclical fluctuation of the price of agricultural products often indirectly affects the inner psychological demand of agricultural consumers. If the prices of production and processing industries, which rely on cheap raw materials as basic raw materials, are subject to frequent and abnormal fluctuations, it is likely to cause further widespread concerns about people’s lives, and ultimately lead to a vicious cycle of falling commodity prices. In recent years, as a result of lack of timely authoritative information all the time, the market price of agricultural products appeared a lot of varieties before short-term rise and fall repeatedly phenomenon. This paper attempts to take the quantity of garlic produced and sold by pork in China as the key object of analysis and research, and analyzes the level of market price index and the main factors influencing the price of pork sales and edible garlic demand in China in recent ten years. In information economics, financial market theory, statistical methods and other relevant mathematical model as the main guidance, combined with China’s agricultural prices during the period of policy, select the RBF neural network theory and the analysis methods for technical improvements, in a variety of market factors affecting economic operation under the mixed operation China period in our country agricultural prices technology wave prediction rule, broken Based on the traditional artificial statistics and forecasting model method of agricultural product price, the short-term forecasting model of Chinese agricultural product market price theory based on information technology innovation method was established.

An improved encoder-decoder-based CNN model for probabilistic short-term load and PV forecasting

An improved encoder-decoder-based CNN model for probabilistic short-term load and PV forecasting

An assessment of stingless beehive climate impact using multivariate recurrent neural networks

<p>A healthy bee colony depends on various elements, including a stable habitat, a sufficient source of food, and favorable weather. This paper aims to assess the stingless beehive climate data and examine the precise short-term forecast model for hive weight output. The dataset was extracted from a single hive, for approximately 36-hours, at every seven seconds time stamp. The result represents the correlation analysis between all variables. The evaluation of root-mean-square error (RMSE), as well as the RMSE performance from various types of topologies, are tested on four different forecasting window sizes. The proposed forecast model considers seven of input vectors such as hive weight, an inside temperature, inside humidity, outside temperature, outside humidity, the dewpoint, and bee count. The various network architecture examined for minimal RMSE are long short-term memory (LSTM) and gated recurrent units (GRU). The LSTM1X50 topology was found to be the best fit while analyzing several forecasting windows sizes for the beehive weight forecast. The results obtained indicate a significant unusual symptom occurring in the stingless bee colonies, which allow beekeepers to make decisions with the main objective of improving the colony’s health and propagation.</p>

Boosted GRU model for short-term forecasting of wind power with feature-weighted principal component analysis

Boosted GRU model for short-term forecasting of wind power with feature-weighted principal component analysis

Hybrid VMD-CNN-GRU-based model for short-term forecasting of wind power considering spatio-temporal features

Accurate and reliable short-term forecasting of wind power is vital for balancing energy and integrating wind power into a grid. A novel hybrid deep learning model is designed in this study to increase the prediction accuracy of short-term wind power forecasting on a wind farm in Jiang County, Shanxi, China. The proposed hybrid deep learning model comprises variable mode decomposition (VMD), convolutional neural network (CNN), and gated recurrent unit (GRU). VMD substantially reduces the volatility of wind speed sequences. CNN automatically extracts complex spatial features from wind power data, and GRU can directly extract temporal features from historical input data. The forecasting accuracy of the combined VMD-CNN-GRU model is higher than that of any single model for wind power. The study used data obtained in 15 min intervals from the wind farm to determine the effectiveness of the proposed model against other advanced models. Compared with the other deep learning models, VMD-CNN-GRU is the best at short-term forecasting, with an RMSE of 1.5651, MAE of 0.8161, MAPE of 11.62%, and R2 of 0.9964. This method is valuable for practical applications and can be used to maintain safe wind farm operations in the future.

Hybrid deep learning models for short-term demand forecasting of online car-hailing considering multiple factors

ABSTRACT As an emerging force in the travel industry, online car-hailing (OCH) has been relying on digital and intelligent technologies for business innovation since its birth. Three hybrid deep learning models combining multi-factor external and internal features are proposed to predict the OCH demand. Convolutional neural network (CNN), long short-term memory (LSTM), bidirectional LSTM (BiLSTM), gated recurrent unit (GRU), and convolutional LSTM (ConvLSTM) are selected to extract features. Attention mechanisms are used to combine the global parts so that the importance of feature sequences at different times can be distinguished. The effectiveness of the proposed models considering all factors is proved by comparative experiments. Then, ablation experiments are performed to analyze the effects of attention module, external and internal factors. The results showed that the hybrid models performed better than the existing models under different factors. Various factors had different impacts on the departure and arrival flows and the hybrid models.

Photovoltaic power electricity generation nowcasting combining sky camera images and learning supervised algorithms in the Southern Spain

The alternation between cloudy and clear skies alters the photovoltaic production. This makes it necessary to anticipate these disturbances hours in advance for the correct operation of the electricity distribution plants and networks. In this paper, two short-term forecasting models (3 h) are developed to forecast the photovoltaic production in an integrated plant in the CIESOL building of the University of Almería. The methodology used is based on sky camera images and Artificial Intelligence techniques. Two models have been developed and compared applying artificial neural network (ANN) and support vector machine (SVM) techniques. The global irradiance predicted using sky camera images is used as an input variable in both models. In addition, the operational status of the plants has been included as an input parameter through the performance ratio. The results have shown that the errors made by ANN and SVM are very similar. For all sky conditions, the uncertainty of the production forecast differs by less than 2% from the uncertainty of the solar resource, which is the main source of error in the production models developed.

A Hybrid Neural Network Model for Short-Term Wind Speed Forecasting

This study proposes an effective wind speed forecasting model combining a data processing strategy, neural network predictor, and parameter optimization method. (a) Variational mode decomposition (VMD) is adopted to decompose the wind speed data into multiple subseries where each subseries contains unique local characteristics, and all the subseries are converted into two-dimensional samples. (b) A gated recurrent unit (GRU) is sequentially modeled based on the obtained samples and makes the predictions for future wind speed. (c) The grid search with rolling cross-validation (GSRCV) is designed to simultaneously optimize the key parameters of VMD and GRU. To evaluate the effectiveness of the proposed VMD-GRU-GSRCV model, comparative experiments based on hourly wind speed data collected from the National Renewable Energy Laboratory are implemented. Numerical results show that the root mean square error, mean absolute error, mean absolute percentage error, and symmetric mean absolute percentage error of this proposed model reach 0.2047, 0.1435, 3.77%, and 3.74%, respectively, which outperform the benchmark predictions using popular parameter optimization methods, data processing techniques, and hybrid neural network forecasting models.

LSTM-based short-term ionospheric TEC forecast model and positioning accuracy analysis

LSTM-based short-term ionospheric TEC forecast model and positioning accuracy analysis

Develop a multi-linear-trend fuzzy information granule based short-term time series forecasting model with k-medoids clustering

In fuzzy information granule (FIG) based short-term forecasting models, the constructed FIG focuses on one of two tasks: capture data characteristic and improve semantic description at a common time concept (time interpretability). For accomplishing both tasks at once, a multilinear-trend fuzzy information granulation algorithm is raised with three-stage time span adjustments, which consistent with the multi-linear-trend information of data and time interpretability. The multilinear-trend FIGs granulated through this algorithm are composed of different number of unequal-size linear FIGs. In order to identify the difference of such granules, k-medoids based multilinear-trend FIG clustering algorithm is put forward, and a cluster label series corresponds to a series of multilinear-trend FIGs can be obtained, where each cluster label represents one kind of multi-linear-trend patterns. Following these, the correlation among patterns will be depicted by a new type fuzzy association rule (FAR), cluster label association rule, which characterizedby simple form so that suitable for practical application. Combing the proposed algorithms with FAR, a novel model for short-term forecasting at granular level is designed, it makes results from available FARs and reduces cumulative errors in prediction. The better forecasting performance of this model has been verified after comparing it with existing models in experiments.

Using additive regression models for short-term forecasting of financial macro-indicators and assessing the potential for financing megaprojects

The subject of this article is the issue of using additive regression models to predict financial indicators at the macro level. At the same time, special attention is paid to the impact of the economy monetization on the possibility of attracting funding for global development projects (megaprojects). It is shown that the main drawback of the most common forecasting models today is their situation-dependent nature. This, in turn, creates difficulties with the initial setup of the models and the subsequent interpretation of the results obtained, limiting the scope of the models, making the use of this toolkit difficult for financial professionals who do not have special mathematical training. With the help of modeling, forecast values of the gross domestic product (GDP) and money supply (M2) for the short-term time obtained, on the basis of which the expected value of the level of the economy monetization was calculated. Based on a predictive assessment of the level of monetization, it is shown that at the moment the country has a limited potential for increasing domestic debt, which, in the conditions of closing access to international capital markets and partial blocking of state reserves, can become a factor in disrupting the financing of megaprojects for the economy structural modernization. Directions for improving the monetary policy aimed at correcting this situation and increasing domestic investment activity are proposed.