Artificial Neural Network Forecast Research Articles

Artificial neural networks as a tool for seasonal forecast of attack intensity of Spodoptera spp. in Bt soybean.

Soybean (Glycine max) is the world's most cultivated legume; currently, most of its varieties are Bt. Spodoptera spp. (Lepidoptera: Noctuidae) are important pests of soybean. An artificial neural network (ANN) is an artificial intelligence tool that can be used in the study of spatiotemporal dynamics of pest populations. Thus, this work aims to determine ANN to identify population regulation factors of Spodoptera spp. and predict its density in Bt soybean. For two years, the density of Spodoptera spp. caterpillars, predators, and parasitoids, climate data, and plant age was evaluated in commercial soybean fields. The selected ANN was the one with the weather data from 25 days before the pest's density evaluation. ANN forecasting and pest densities in soybean fields presented a correlation of 0.863. It was found that higher densities of the pest occurred in dry seasons, with less wind, higher atmospheric pressure and with increasing plant age. Pest density increased with the increase in temperature until this curve reached its maximum value. ANN forecasting and pest densities in soybean fields in different years, seasons, and stages of plant development were similar. Therefore, this ANN is promising to be implemented into integrated pest management programs in soybean fields.

Validation Challenges in Data for Different Diesel Engine Performance Regimes Utilising HVO Fuel: A Study on the Application of Artificial Neural Networks for Emissions Prediction

Artificial neural networks (ANNs) provide supervised learning via input pattern assessment and effective resource management, thereby improving energy efficiency and predicting environmental fluctuations. The advanced technique of ANNs forecasts diesel engine emissions by collecting measurements during trial sessions. This study included experimental sessions to establish technical and ecological indicators for a diesel engine across several operational scenarios. VALLUM01, a novel tool, has been created with a user-friendly interface for data input/output, intended for the purposes of testing and prediction. There was a comprehensive collection of 12 input parameters and 10 output parameters that were identified as relevant and sufficient for the objectives of training, validation, and prediction. The proper value ranges for transforming into fuzzy sets for input/output to an ANN were found. Given that the ANN’s training session comprises 1,000,000 epochs and 1000 perceptrons within a single-hidden layer, its effectiveness can be considered high. Many statistical distributions, including Pearson, Spearman, and Kendall, validate the prediction accuracy. The accuracy ranges from 96% on average, and in some instances, it may go up to 99%.

Exploring tensile properties of bio composites reinforced date palm fibers using experimental and Modelling Approaches

The objective of this study was to assess the tensile strength of epoxy bio-composites reinforced with palm fibers, both untreated and treated with sodium carbonate NaHCO3 at a concentration of 10 % (w/v) for 24 and 96 h, with varying weight percentages of fibers (15 %, 20 %, 25 %, and 30 %). To predict the mechanical performance of the composites, two methods were employed: artificial neural network (ANN) and response surface methodology (RSM). A Box-Behnken RSM design was used to conduct experiments and establish a mathematical model of the bio-composite behavior as a function of the fiber percentage in the samples, specimen cross-section, and treatment time. The ANN forecasts showed consistent expected values for the bio-composite sample behavior, with a correlation coefficient (R2) greater than 0.98 for Young's modulus and 0.97 for stress. Similarly, the correlation coefficients obtained by RSM for the mechanical properties were also highly satisfactory, with an R2 of 0.89 for Young's modulus and 0.87 for stress. Finally, the errors generated by each method (Box-Behnken and ANN) were compared to the experimental results.

Aquaponics for Trinidad and Tobago: Advancing Sustainable Farming for Low-carbon Economy

Trinidad and Tobago (T&T) is a net importer of food. Thus, the country is vulnerable to high food prices, high food imports, and food insecurity. These problems can be addressed through the development of the aquaponics industry locally. Moreover, aquaponics offers a sustainable solution as it encourages the conservation of water, and eliminates the need for synthetic fertilizer, which generates high greenhouse gas (GHGs) emissions in its production process. The objectives of this study are to analyze and forecast T&T’s food imports, and to examine the technical and economic policy requirements for an aquaponics industry in T&T. An Empirical Mode Decomposition (EMD)-Artificial Neural Network (ANN) forecast of T&T’s food imports suggests that T&T’s food imports will increase, and by month 12 to will be TT$1,224,329,616.27. With regards to the economic framework, the major obstacle to the development of T&T’s aquaponics industry appears to be the upfront costs. To establish a professional aquaponics farm that maintains supplies of fish and plant produce, the farmer must incur an upfront capital cost. If the cost of the greenhouse is included, it can take a farmer up to 6 years to recover this cost before they earn a profit. Therefore, government support is required to alleviate this cost burden for farmers.

Banana/ coir biofibers and carbon/innegra fabrics and BN/MWCNT nanoparticles reinforced UV resistant polyester hybrid composites

Plant cellulose fibers are a biodegradable and acceptable alternative for fabricating balanced-strength polymer laminate components due to their higher specific strength, eco-friendliness, and renewable nature. The target of the present investigation is to manufacture banana-boron nitride-carbon fabric and coir-multi walled carbon nanotubes-Innegra fabric hybrid polyester laminates by varying different chemical treatments and determine the physical (density, water absorption, and contact angle), mechanical (tensile, flexural, interlaminar shear, impact strength, and shore D hardness) and thermal (onset, endset, glass transition temperature, and carbon residue) characteristics of fabricated laminates. The addition of chemically treated/ untreated banana/ coir fillers in the polyester matrix was affirmed morphologically and structurally from Fourier Transform Infra-Red (FTIR) spectra and scanning electron microscope (SEM). Differential scanning calorimeter (DSC) and Thermogravimetric analysis (TGA) results examined the higher thermal stability of the chemically treated banana/ coir fiber reinforced polyester hybrid laminates around 400 °C. The artificial neural network modeling was performed to select the best composite material among the fabricated laminates. The determination coefficient values were lower than 0.98 for testing, validation, and training for both laminates. The model outcomes exhibited lower root mean squared error, explaining that using the model of an exact threshold was predicted for the artificial neural network forecasting. The one way-Analysis of variance (ANOVA) technique was applied to study the significance of the difference between the chemically treated/ untreated banana and coir fillers on physical, mechanical, and thermal characteristics.

Artificial neural network assisted robust droop control of autonomous microgrid

AbstractElectric grid is vulnerable to power imbalance and inertia is the grid's response to overcome such disturbance. Augmentation of power electronic converter based renewable energy technologies like Photovoltaic Generators (PVG) and batteries in utility grid significantly reduces inertia. Inertia degradation is indicated by sharp Rate of Change of Frequency (ROCOF) events due to any grid component failure or imbalance. Fixed gain feedback Proportional Integral Derivative (PID) control is insufficient to deal with varying ROCOF events. This work proposes Sliding Mode (SM) robust droop control scheme assisted by Artificial Neural Network (ANN) algorithm for an islanded PVG integrated microgrid. Droop response is governed by swing equation that uses PVG Maximum Power Point (MPP) forecasted by ANN. ANN forecast is compared with optimized Gaussian process regression algorithm based on mean squared error and speed of training as key performance indicator. The algorithms are trained and validated based on climate dataset of Islamabad, Pakistan. SM control performance is compared with various PID gain settings and qualified as the most suitable against variable source, load and ROCOF scenarios. Finally, significance of accurate MPP forecast for droop control is established by comparing the ANN and deterministic forecaster assisted droop response in a microgrid case study.

Demand forecasting of spare parts with regression and machine learning methods: Application in a bus fleet

Forecasting the demand of spare parts of vehicles in bus fleets is a vital issue. Vehicles must operate effectively and must have a high availability rate in the fleet. In maintenance operations, faulty parts or parts that complete their lifetime must be replaced with a new one. Spare parts needed must be in inventories with the required amount on time. In this sector, there are thousands of spare parts to manage. The maintenance and repair department must operate effectively. In order to accomplish this, accurate forecast of spare parts is required. In this study, demand forecasting was carried out with regression-based methods (multivariate linear regression, multivariate nonlinear regression, Gaussian process regression, additive regression, regression by discretion, support vector regression), rule-based methods (decision table, M5Rule), tree-based methods (random forest, M5P, Random tree, REPTree) and artificial neural networks. The forecasting model developed in this study includes critical variables such as the number of vehicles in the fleet, the number of breakdowns that cause parts to change, the number of periodic maintenance, mean time between failure and demand quantity in previous years. The application was carried out with real data of eight (2013–2020) years. 2013–2019 data was used for training and 2020 data was used for testing. In forecasts, support vector regression among regression-based methods, decision table among rule-based methods, M5P among tree-based methods gave the best results. It has been observed that the artificial neural network produced more accurate forecasts than all other methods. Artificial neural network forecasts give the highest forecast accuracy rate and the least deviation.

A novel smoothing-based long short-term memory framework for short-to medium-range flood forecasting

ABSTRACT A novel smoothing-based long short-term memory (Smooth-LSTM) framework for flood forecasting up to five days ahead is proposed, and compared with the benchmark LSTM (LSTM) model, an Artificial Neural Network (ANN) model, and the conceptual Nedbør Afstrømnings Model (MIKE11 NAM)-Hydrodynamic (HD) (MIKE) hydrological model. This framework was tested in the typical middle Mahanadi River basin (India), which has a tropical monsoon-type climate. Variation of training loss indicated the LSTM network has a higher learning ability at smaller network and batch sizes. The Smooth-LSTM model could predict streamflow with higher Nash-Sutcliffe efficiency of 0.82–0.87 at up to five days lead time with a better reproduction of the observed crucial high peak floods, whereas the corresponding MIKE, ANN and LSTM model-based forecasts were acceptable only up to four-, three- and one-day lead times, respectively. Overall, the Smooth-LSTM model is found to be robust in operational flood forecasting, with lower uncertainty and the least sensitivity to redundant input information.

Short-Term Electricity Price Forecasting Based on BP Neural Network Optimized by SAPSO

In the electricity market environment, the market clearing price has strong volatility, periodicity and randomness, which makes it more difficult to select the input features of artificial neural network forecasting. Although the traditional back propagation (BP) neural network has been applied early in electricity price forecasting, it has the problem of low forecasting accuracy. For this reason, this paper uses the maximum information coefficient and Pearson correlation analysis to determine the main factors affecting electricity price fluctuation as the input factors of the forecasting model. The improved particle swarm optimization algorithm, called simulated annealing particle swarm optimization (SAPSO), is used to optimize the BP neural network to establish the SAPSO-BP short-term electricity price forecasting model and the actual sample data are used to simulate and calculate. The results show that the SAPSO-BP price forecasting model has a high degree of fit and the average relative error and mean square error of the forecasting model are lower than those of the BP network model and PSO-BP model, as well as better than the PSO-BP model in terms of convergence speed and accuracy, which provides an effective method for improving the accuracy of short-term electricity price forecasting.

Optimizing the input vectors of applied artificial neural network models for wind power production forecasting

The significant increase of wind energy production worldwide revealed the necessity of its accurate forecasting. However, this is a very complex and, despite the progress made, more accurate forecasting methods are still needed. Accurate forecasts will contribute to a better power plant and grid management by solving problems related to the distribution and storage of the produced electricity, maximizing thus the profits of wind energy investments, contributing ultimately to their further enhancement. Here we present the development and validation of selected artificial neural network (ANN) wind energy forecasting models that produce hourly forecasts for 24 hours forecast horizon. The models are developed and validated using wind speed, direction, and energy produced by a wind power plant located at a semi-mountainous area in Western Greece. The ANN forecasts are compared against those of the persistence method using the Root Means Square Error (RMSE) and the Mean Absolute Percentage Error (MAPE) statistics.

Effects of Model Structural Complexity and Data Pre-Processing on Artificial Neural Network (ANN) Forecast Performance for Hydrological Process Modelling

The choice of a particular Artificial Neural Network (ANN) structure is a seemingly difficult task; worthy of relevance is that there is no systematic way for establishing a suitable architecture. In view of this, the study looked at the effects of ANN structural complexity and data pre-processing regime on its forecast performance. To address this aim, two ANN structural configurations: 1) Single-hidden layer, and 2) Double-hidden layer feed-forward back propagation network were employed. Results obtained revealed generally that: a) ANN comprised of double hidden layers tends to be less robust and converges with less accuracy than its single-hidden layer counterpart under identical situations; b) for a univariate time series, phase-space reconstruction using embedding dimension which is based on dynamical systems theory is an effective way for determining the appropriate number of ANN input neurons, and c) data pre-processing via the scaling approach excessively limits the output range of the transfer function. In specific terms considering extreme flow prediction capability on the basis of effective correlation: Percent maximum and minimum correlation coefficient (Rmax% and Rmin%), on the average for one-day ahead forecast during the training and validation phases respectively for the adopted network structures: 8 7 5 (i.e., 8 input nodes, 7 nodes in the hidden layer, and 5 output nodes in the output layer), 8 5 2 5 (8 nodes in the input layer, 5 nodes in the first hidden layer, 2 nodes in the second hidden layer, and 5 nodes in the output layer), and 8 4 3 5 (8 nodes in the input layer, 4 nodes in the first hidden layer, 3 nodes in the second hidden layer, and 5 nodes in the output layer) gave: 101.2, 99.4; 100.2, 218.3; 93.7, 95.0 in all instances irrespective of the training algorithm (i.e., pooled). On the other hand, in terms of percent of correct event prediction, the respective performances of the models for both low and high flows during the training and validation phases, respectively were: 0.78, 0.96: 0.65, 0.87; 0.76, 0.93: 0.61, 0.83; and 0.79, 0.96: 0.65, 0.87. Thus, it suffices to note that on the basis of coherence or regularity of prediction consistency, the ANN model: 8 4 3 5 performed better. This implies that though the adoption of large hidden layers vis-à-vis corresponding large neuronal signatures could be counter-productive because of network over-fitting, however, it may provide additional representational power. Based on the findings, it is imperative to note that ANN model is by no means a substitute for conceptual watershed modelling, therefore, exogenous variables should be incorporated in streamflow modelling and forecasting exercise because of their hydrologic evolutions.

Intelligent Optimization Framework for Efficient Demand-Side Management in Renewable Energy Integrated Smart Grid

The implementation of real-time price-based demand response program and integration of renewable energy resources (RESs) improves efficiency and ensure stability of electric grid. This paper proposes a novel intelligent optimization based demand-side management (DSM) framework for smart grid integrated with RESs. In the intelligent DSM framework the artificial neural network (ANN) forecasts energy usage behavior of consumers and real-time price-based demand response program (RTPDRP) of electric utility company (EUC). The smart energy management controller of the proposed intelligent DSM framework adapts forecasted energy usage behavior of consumers using forecasted RTPDRP to create operation schedule. The consumers implement the created schedule to minimize energy cost, peak load, carbon emission subjected to improving user comfort and avoiding rebound peaks. Simulations are conducted using our proposed hybrid genetic ant colony (HGAC) optimization algorithm to create schedule for three cases: EUC without RESs, EUC with RESs, and EUC with both RESs and storage technologies. To endorse the applicability and productivity of the proposed DSM framework based on HGAC optimization algorithm with five existing algorithms based frameworks. Simulation results show that the proposed DSM framework is superior compared with the existing frameworks in terms of energy cost minimization, peak load mitigation, carbon emission alleviation, and user discomfort minimization. The proposed HGAC optimization algorithm reduced electricity cost, carbon emission, and peak load by 12.16%, 4.00%, and 19.44% in case I; by 26.8%, 20.71%, and 33.3% in case II; and by 24.4%, 16.44%, and 37.08% in case III, respectively, compared to without scheduling.

Impact of COVID-19 on GDP of major economies: Application of the artificial neural network forecaster

The ongoing COVID-19 pandemic has caused global health impacts, and governments have restricted movements to a certain extent. Such restrictions have led to disruptions in economic activities. In this paper, the GDP figures for the April–June quarter of 2020 for eight countries, namely, the United States, Mexico, Germany, Italy, Spain, France, India, and Japan, are forecasted. Considering that artificial neural network models have higher forecasting accuracy than statistical methods, a multilayer artificial neural network model is developed in this paper. This model splits the dataset into two parts: the first with 80% of the observations and the second with 20%. The model then uses the first part to optimize the forecasting accuracy and then applies the optimized parameters to the second part of the dataset to assess the model performance. A forecasting error of less than 2% is achieved by the model during the testing procedure. The forecasted GDP figures show that the April–June quarter of the current year experienced sharp declines in GDP for all countries. Moreover, the annualized GDP growth is expected to reach double-digit negative growth rates. Such alarming prospects require urgent rescue actions by governments.

Artificial neural network approach for co-pyrolysis of Chlorella vulgaris and peanut shell binary mixtures using microalgae ash catalyst

Abstract The catalytic pyrolysis of pure microalgae (M), peanut shell wastes (PS) and their binary mixtures were analysed by introducing the microalgae ash (MA) as a catalyst. The pyrolysis processes were conducted at different heating rates from 10 K/min-100 K/min to observe their thermal degradation behaviour. Additionally, Artificial Neural Network (ANN) was applied by feeding the heating rates and temperatures to predict the weight loss of the samples. The kinetic and thermodynamic parameters were also determined through three different iso-conversional kinetic models: Friedman (FR), Kissinger-Akahira-Sunose (KAS) and Flynn-Wall-Ozawa (FWO). Based on the kinetic results, FWO model achieved the lowest deviation between the activation energies (Ea) from the experimental which aligned with the ANN predicted results. The finding also shows that the activation energy (Ea) of the catalytic pyrolysis of binary mixtures was lower than the pure M and PS (Experimental: 142.56 kJ/mol; ANN forecast: 131.37 kJ/mol).

Good times bad times: Automated forecasting of seasonal cryptosporidiosis in Ontario using machine learning

The rise of big data and related predictive modelling based on machine learning algorithms over the last two decades have provided new opportunities for disease surveillance and public health preparedness. Big data come with the promise of faster generation of and access to more precise information, potentially facilitating predictive precision in public health ("precision public health"). As an example, we considered forecasting of the future course of the monthly cryptosporidiosis incidence in Ontario. The traditional statistical approach to forecasting is the seasonal autoregressive integrated moving-average (SARIMA) model. We applied SARIMA and an artificial neural network (ANN) approach, specifically a feed-forward neural network, to predict monthly cryptosporidiosis incidence in Ontario in 2017 using 2005-2016 data as a training set. Both forecasting approaches are automated to make them relevant in a disease surveillance context. We compared the resulting forecasts using the root mean squared error (RMSE) and mean absolute error (MAE) as measures of predictive accuracy. Cryptosporidiosis is a seasonal disease, which peaks in Ontario in late summer. In this study, the SARIMA model and ANN forecasting approaches captured the seasonal pattern of cryptosporidiosis well. Contrary to similar studies reported in the literature, the ANN forecasts of cryptosporidiosis were slightly less accurate than the SARIMA model forecasts. The ANN and SARIMA approaches are suitable for automated forecasting of public health time series data from surveillance systems. Future studies should employ additional algorithms (e.g. random forests) and assess accuracy by using alternative diseases for case studies and conducting rigorous simulation studies. Difference between the forecasts from the machine learning algorithm, that is, the ANN, and the statistical learning model, that is, the SARIMA, should be considered with respect to philosophical differences between the two approaches.

A machine learning based prediction system for the Indian Ocean Dipole

The Indian Ocean Dipole (IOD) is a mode of climate variability observed in the Indian Ocean sea surface temperature anomalies with one pole off Sumatra and the other pole near East Africa. An IOD event starts sometime in May-June, peaks in September-October and ends in November. Through atmospheric teleconnections, it affects the climate of many parts of the world, especially that of East Africa, Australia, India, Japan, and Europe. Owing to its large impacts, previous studies have addressed the predictability of the IOD using state of the art coupled climate models. Here, for the first-time, we predict the IOD using machine learning techniques, in particular artificial neural networks (ANNs). The IOD forecasts are generated for May to November from February-April conditions. The attributes for the ANNs are derived from sea surface temperature, 850 hPa and 200 hPa geopotential height anomalies, using a correlation analysis for the period 1949–2018. An ensemble of ANN forecasts is generated using 500 samples with replacement using jackknife approach. The ensemble mean of the IOD forecasts indicates the machine learning based ANN models to be capable of forecasting the IOD index well in advance with excellent skills. The forecast skills are much superior to the skills obtained from the persistence forecasts that one would guess from the observed data. The ANN models also perform far better than the models of the North American Multi-Model Ensemble (NMME) with higher correlation coefficients and lower root mean square errors (RMSE) for all the target months of May-November.

Home Energy Management System for a domestic load center using Artificial Neural Networks towards Energy Integration

This paper titled ‘Home Energy Management System (HEMS) for a domestic load center using Artificial Neural Networks towards Energy Integration’ focuses on implementing intelligent integration of Distribution Generation Systems (DGS) for domestic load. The proposed energy integration by HEMS, is executed by implementing a Load Management Algorithm (LMA), which functions based on ANN forecast models. Demand Side Management (DSM) techniques are the back bone for LMA proposed in this paper. Historical temperature data and electrical load data of a domestic load center at Municipality of Birmingham, City of Alabama, USA, is considered to implement the proposed LMA. Two subroutine programs/algorithms are designed to implement the proposed LMA. First is of implementing Load Priority Techniques, to assign Load Priority for the loads for the hour based on the temperature forecast. Temperature forecast is done using Nonlinear Auto Regression with Exogenous Inputs (NARX) ANN time series model. And this is termed as Load Priority Assignment Algorithm (LPAA). Second is of predicting Threshold Power (PTh) for the hour as per load priority assigned by LPAA, this is termed as estimation of PTh. Big data driven Nonlinear Auto Regression with Exogenous Inputs (NARX) Neural Net based temperature forecasting model is implemented and used to assign hourly priority for all the appliances/loads. Big data driven Artificial Neural Network (ANN) based load power forecasting model is implemented and used to predict hourly PTh, LMA proposed for HEMS is implemented with step by step comparison of current load demand with PTh and transferring the loads between the energy sources, according to the load priority assignment from LPAA. Proposed LMA based HEMS contributes to enhance the penetration of nonconventional DGS into domestic load sector. Simulation of proposed HEMS is implemented in MATLAB-Simulink environment using MATLAB 2018b. Simulation model is embedded into target hardware: Arduino 2560 by enabling serial communication protocols between MATLAB-SIMULINK and ARDUINO. Hence the experimental setup of HEMS would be functional to prove simulation results and to transfer the loads in real time. Working Hardware model of the HEMS at domestic load center is fabricated using simple and cost effective components such as current sensor ACS712, Arduino Mega 2560, relays and relay driver circuit.

A Review of Epidemic Forecasting Using Artificial Neural Networks

Background and aims: Since accurate forecasts help inform decisions for preventive health-care intervention and epidemic control, this goal can only be achieved by making use of appropriate techniques and methodologies. As much as forecast precision is important, methods and model selection procedures are critical to forecast precision. This study aimed at providing an overview of the selection of the right artificial neural network (ANN) methodology for the epidemic forecasts. It is necessary for forecasters to apply the right tools for the epidemic forecasts with high precision. Methods: It involved sampling and survey of epidemic forecasts based on ANN. A comparison of performance using ANN forecast and other methods was reviewed. Hybrids of a neural network with other classical methods or meta-heuristics that improved performance of epidemic forecasts were analysed. Results: Implementing hybrid ANN using data transformation techniques based on improved algorithms, combining forecast models, and using technological platforms enhance the learning and generalization of ANN in forecasting epidemics. Conclusion: The selection of forecasting tool is critical to the precision of epidemic forecast; hence, a working guide for the choice of appropriate tools will help reduce inconsistency and imprecision in forecasting epidemic size in populations. ANN hybrids that combined other algorithms and models, data transformation and technology should be used for an epidemic forecast.

Artificial neural network forecast of monthly mean daily global solar radiation of selected locations based on time series and month number

Prediction and forecast of monthly mean daily global solar energy available at selected locations are carried out using a nonlinear autoregressive, a nonlinear autoregressive (exogenous) and a hybrid time series methods. The methods are implemented with artificial neural networks. The proposed hybrid method is based on a combination of nonlinear autoregressive and structural artificial neural networks where the structural input is the current month number. The aim of the hybridization is to improve prediction accuracy of the nonlinear autoregressive methods and simplify the input layer of the nonlinear autoregressive (exogenous) method. Based on various statistical criteria, the hybrid method is verified to predict and forecast global solar energy with noticeably higher accuracy. For example, while a nonlinear autoregressive predicts the global solar energy availability at Abuja with R2-value of 0.78 and correlation coefficient of 0.89, the hybrid method improves the prediction to 0.96 and 0.98. The hybrid model is verified via one-way analysis of variance to consistently perform better than the nonlinear autoregressive method in months ahead forecasting of solar energy. The proposed hybrid method is capable of long-term forecast of up to two years ahead within a typical mean percent forecast error of 5.67%, therefore, is applicable for designing/planning solar energy integration and simulation of agricultural food security, especially in developing countries. A cases study of power output and sizing of standalone PV options showed that decisions based on forecasted and actual solar energy are the same demonstrating the very useful application of the hybrid method.

Intellectualization of logistic supply chains on the basis of forecasting volumes of cargo transportation

Research shows that the accelerated economic movement towards a more intelligent model of development and rapid dissemination of knowledge contributes to the emergence of new modern logistics technologies and approaches to managing global supply chains. The dominant feature of the sustainable development of transport services is the invariant concept of management, the basis of which is the socioecological and economic model. Indicators of the transport sector show a decline in volumes of transportation, but the positive dynamics of growth is demonstrated by the agrarian sector, which produces half of all export volumes and provides one third of the currency revenues of the state.Factors that inhibit the effective functioning of the logistics chain are revealed. In particular, the technological orientation of railways, mainly on route transportation, highlighted the problem of uneven development of ports at ports and railway ports, as well as inconsistencies in grain volumes in Stevedoring companies’ deals with real port facilities. In fact, there is a schedule of seasonal fluctuations in the transportation of cereals, in particular, their absence for 3-4 months per year.The rational model of management and intellectualization of grain supply logistic chain, which takes into account the indicators of artificial neural network forecasting and seasonal grain transportation parameter, is proposed.