Forecasting Tool Research Articles

Estimation of Solar Power Generation Through the Use of Effective Forecasting Algorithms

The unpredictable nature of solar power generation poses significant challenges to energy management, particularly in power grids with high solar penetration, leading to potential imbalances between supply and demand. This has increased interest in developing accurate forecasting methods for photovoltaic power generation. This study focuses on short-term forecasting of solar power generation using MATLAB, based on historical generation trends through three different hybrid models. It uses a multilayer perceptron neural network as the forecasting tool, which is optimized through various algorithms to fine-tune the network's hyperparameters, such as the weights and biases of the input and hidden layers, for optimal performance. The Root Mean Square Error is used as the objective metric to minimize the discrepancy between actual and predicted values. The models use Particle Swarm Optimization (PSO), Imperialistic Competitive Algorithm, and Genetic Algorithm for network optimization. This research analyzes PV generation data from Belgium in July and August 2022, taken at 15-minute intervals, and evaluates model performance using six different error metrics, including Mean Absolute Error. The results show the accuracy and reliability in forecasting solar power generation time series.

Apple, Microsoft, and Amazon stock price prediction based on ARIMA and LSTM

Along with the advancement in technology and data analysis, efforts have been made to use various factors to predict the stock market to improve portfolio performance and ensure an efficient market. This study explores two stock forecasting models, Long Short-Term Memory(LSTM) and Auto Regressive Integrated Moving Average (ARIMA), and evaluates their performance on the same datasets of historical stock prices of Apple, Microsoft, and Amazon. While ARIMA exhibited superior performance in terms of Root Mean Squared Error(RMSE) and R2 metrics, the research acknowledges inherent limitations, such as the exclusion of potential external influencing factors and the simplistic approach toward LSTM optimization. Despite ARIMA's commendable forecasting ability in this context, the dynamic and non-linear nature of stock prices suggests the potential of hybrid models and broader datasets for future research. The findings underscore the significance of selecting appropriate forecasting tools in the volatile domain of stock investments and pave the way for a more holistic and informed approach to stock price prediction.

Predictive Analysis for Supply Chain Management Using Extreme Gradient Boost Classifier

A novel approach to predictive analysis and demand forecasting within supply chain management, employing the Extreme Gradient Boosting (XGBoost) classifier. In response to the escalating complexity and volatility of supply chains, accurate demand forecasting is imperative for optimizing inventory, production scheduling, and overall operational efficiency. Traditional forecasting methods often struggle to capture the nonlinear relationships and intricate patterns inherent in supply chain data. Conversely, XGBoost offers a potent machine learning technique adept at handling nonlinear relationships and delivering robust predictions. The proposed framework involves data preprocessing, feature engineering, model training, and validation stages. Through a case study employing real-world supply chain data, we demonstrate the superior performance of the XGBoost classifier over traditional methods in terms of accuracy, robustness, and scalability. This study underscores XGBoost's potential as a valuable tool for demand forecasting in supply chain management, facilitating informed decision-making, optimized inventory management, cost reduction, and enhanced customer satisfaction. Furthermore, the framework's adaptability and extendibility make it applicable to diverse industries and domains, contributing to the advancement of supply chain management through the integration of machine learning techniques for more precise and efficient demand forecasting

DeepFore: A Deep Reinforcement Learning Approach for Power Forecasting in Renewable Energy Systems

An open network known as the “energy internet” links every component of the whole energy supply chains, from the generations. Due to their ability to mimic regional flow dynamics that have an impact on wind farm production, regional meteorological models are increasingly being used as a general tool for wind resource forecasting. In this study, higher vertical and horizontal resolutions WRF (weather research and forecasting) paradigm simulation are used to anticipate and validate production for a genuine onshore wind farm. This paper proposed a DeepFore which is a power forecasting system for hybrid renewable energy systems. Initially, the dataset is generated by the hybrid system. This data is preprocessed to improve the quality of the data by incorporating, filtering and outlier detection techniques. Then, this enriched data is fed into K++ means clustering algorithm to separate the normal data from faulty data. With the normal and original data, Teaching-Learning based optimization algorithm attempts to realize the optimal features which are important for forecasting. Finally, Deep SARSA which is deep reinforcement learning algorithm is incorporated to determine the power generated by the hybrid system. Better winds energy prediction estimations enable more efficient utilization of the produced electricity, according to computational models.

Advancing mortality rate prediction in European population clusters: integrating deep learning and multiscale analysis

Accurately predicting population mortality rates is crucial for effective retirement insurance and economic policy formulation. Recent advancements in deep learning time series forecasting (DLTSF) have led to improved mortality rate predictions compared to traditional models like Lee-Carter (LC). This study focuses on mortality rate prediction in large clusters across Europe. By utilizing PCA dimensionality reduction and statistical clustering techniques, we integrate age features from high-dimensional mortality data of multiple countries, analyzing their similarities and differences. To capture the heterogeneous characteristics, an adaptive adjustment matrix is generated, incorporating sequential variation and spatial geographical information. Additionally, a combination of graph neural networks and a transformer network with an adaptive adjustment matrix is employed to capture the spatiotemporal features between different clusters. Extensive numerical experiments using data from the Human Mortality Database validate the superiority of the proposed GT-A model over traditional LC models and other classic neural networks in terms of prediction accuracy. Consequently, the GT-A model serves as a powerful forecasting tool for global population studies and the international life insurance field.

Comparative Analysis of ARIMA and LSTM Models for Agricultural Product Price Forecasting

The fluctuations in vegetable prices can have an impact on the economy. Machine learning can identify price trend changes. This study investigates the performance of ARIMA and LSTM models in predicting price trends for agricultural products, focusing on greens and lotus roots. The objective was to ascertain the superior model in terms of accurately reflecting market oscillations—a critical aspect for stakeholders in the agricultural sector. The investigation contrasted the ARIMA model's adeptness at detecting linear tendencies against the LSTM's capacity to decode intricate nonlinear dynamics. Empirical assessment employing metrics such as RMSE, MAE, and SMAPE disclosed a consistent supremacy of the ARIMA model over the LSTM in both datasets. This was particularly evident within the lotus root forecasts, where the discrepancy in error metrics for LSTM was remarkably pronounced. The outcomes indicate that notwithstanding the sophisticated structure of LSTM, ARIMA models maintain their status as robust and precise tools for agricultural time series forecasting. This is especially pertinent in contexts demanding computational efficiency and model interpretability. This investigation evaluates the efficacy of ARIMA and LSTM models in forecasting time-series data for agricultural commodities.

An Intelligent Hybrid Machine Learning Model for Sustainable Forecasting of Home Energy Demand and Electricity Price

Home energy systems (HESs) face challenges, including high energy costs, peak load impact, and reliability issues associated with grid connections. To address these challenges, homeowners can implement solutions such as energy management, renewable resources, and energy storage technologies. Understanding consumption patterns and optimizing HES operations are crucial for effective energy management. As a primary step, addressing these concerns requires an efficient forecasting tool to predict home energy demand and electricity prices. Due to the complexity of big data, and uncertainties involved in forecasting, machine learning (ML) methods are necessary. In this study, we develop a hybrid machine learning approach, utilizing one year of data on home energy demand and prices to address the challenge of forecasting home energy consumption. A comprehensive comparison of different deep and non-deep ML models highlights the superiority of the proposed hybrid approach. The performance of these models, measured using metrics such as RMSE, MAE, R2, and RT (running time), are compared. Finally, an optimized hybrid XGBoost (XGB) ML model that combines price and energy demand forecasting is introduced. The proposed ML method’s parameters are optimally determined using Particle Swarm Optimization. The hybrid ML model’s performance is evaluated in predicting both energy demand and consumption prices using historical data from diverse households with various features and consumption patterns. The results indicate that the hybrid ML model achieves accurate predictions for energy consumption and prices, with improvements in RMSE (up to 36.6%), MAE (up to 36.8%), and R2 (up to 3.9), as compared to conventional ML methods. This research contributes to sustainable energy practices by providing an effective tool for forecasting energy consumption and associated costs in the dynamic landscape of home energy systems.

Analyse and Predict the Detection of the Cyber - Attack Process by Using a Machine-Learning Approach

Crimes committed online rank among the most critical global concerns. Daily, they cause country and citizen economies to suffer massive financial losses. With the proliferation of cyber-attacks, cybercrime has also been on the rise. To effectively combat cybercrime, it is essential to identify its perpetrators and understand their methods. Identifying and preventing cyber-attacks are difficult tasks. To combat these concerns, however, new research has produced safety models and forecast tools grounded on artificial intelligence. Numerous methods for predicting criminal behaviour are available in the literature. While they may not be perfect, they may help in cybercrime and cyber-attack tactic prediction. To find out whether an attack happened and, if so, who was responsible, one way to look at this problem is by using real-world data. There is data about the crime, the perpetrator's demographics, the amount of property damaged, and the entry points for the assault. Potentially, by submitting applications to forensics teams, victims of cyber-attacks may get information. This study uses ML methods to analyse cyber-crime consuming two patterns and to forecast how the specified characteristics will furnish to the detection of the cyber-attack methodology and perpetrator. Based on the comparison of eight distinct machine-learning methods, one can say that their accuracy was quite comparable. The Support Vector Machine (SVM) Linear outperformed all other cyber-attack tactics in terms of accuracy. The initial model gave us a decent notion of the assaults that the victims would face. The most successful technique for detecting malevolent actors was logistic regression, according to the success rate. To anticipate who the perpetrator and victim would be, the second model compared their traits. A person’s chances of being a victim of a cyber-attack decrease as their income and level of education rise. The proposed idea is expected to be used by departments dealing with cybercrime. Cyber-attack identification will also be made easier, and the fight against them will be more efficient.

Earthquake forecasting from paleoseismic records

Forecasting large earthquakes along active faults is of critical importance for seismic hazard assessment. Statistical models of recurrence intervals based on compilations of paleoseismic data provide a forecasting tool. Here we compare five models and use Bayesian model-averaging to produce time-dependent, probabilistic forecasts of large earthquakes along 93 fault segments worldwide. This approach allows better use of the measurement errors associated with paleoseismic records and accounts for the uncertainty around model choice. Our results indicate that although the majority of fault segments (65/93) in the catalogue favour a single best model, 28 benefit from a model-averaging approach. We provide earthquake rupture probabilities for the next 50 years and forecast the occurrence times of the next rupture for all the fault segments. Our findings suggest that there is no universal model for large earthquake recurrence, and an ensemble forecasting approach is desirable when dealing with paleoseismic records with few data points and large measurement errors.

Atmospheric Rivers are Responsible for Cyclicity in Sierra Nevada Precipitation

Abstract Cool-season (November–March) precipitation contributes critically to California’s water resources and flood risk. In the Sierra Nevada, approximately half of cool-season precipitation is derived from a small proportion of storms classified as atmospheric rivers (ARs). The frequency and intensity of ARs are highly variable from year to year and unreliable climate teleconnections limit forecasting. However, previous research provides intriguing evidence of cycles with biennial (2.2 years) and decadal (10–20 years) periodicities in Sierra Nevada cool-season precipitation, suggesting it is not purely stochastic. To identify the source of this cyclicity, we decompose daily precipitation records (1949–2022) into contributions from ARs versus non-ARs, as well as into variations in frequency and intensity. We find that the biennial and decadal spectral peaks in Sierra Nevada precipitation totals are entirely due to precipitation delivered by ARs, and primarily due to variations in the frequency of days with AR precipitation. While total non-AR precipitation correlates with sea surface temperature (SST) and atmospheric pressure patterns associated with the El Niño–Southern Oscillation, AR precipitation shows no consistent remote teleconnections at any periodicity. Supporting this finding, atmospheric simulations forced by observed SSTs do not reproduce the biennial or decadal precipitation variations identified in observations. These results, combined with the lack of long-term stable cycles in previously published tree-ring reconstructions, suggest that the observed biennial and decadal quasi-cyclicity in Sierra Nevada precipitation is unreliable as a forecasting tool. Significance Statement In California’s Sierra Nevada, where most of the state’s above-ground water resources originate, cool-season precipitation totals exhibited year-to-year and decadal cyclicity over the past century. Long-range forecasts are notoriously unskillful in this region, so nonrandom cycles would be intriguing to water managers challenged to simultaneously minimize flood and drought risk. Over 1949–2022, precipitation cycles were driven by variations in the number of atmospheric river (AR) storms per year even though ARs account for just half of total precipitation. These findings bring us a step closer to understanding the causes of precipitation cyclicity, but we find no evidence that the cycles were underpinned by larger-scale ocean–atmosphere circulations so we caution against relying on continued cycles into the future.

The Generalized Long-Term Fault Memory Model and Applications to Paleoseismic Records

ABSTRACT Paleoseismic studies show large variability in earthquake interevent times along a fault, with short intervals often separated by long quiescent periods. Some paleoseismologists have interpreted this variability as a product of an earthquake’s partial strain release with the next earthquake occurring sooner than expected because of the remaining residual strain. However, commonly used probabilistic large earthquake recurrence models attribute this variability purely to chance, not the state of strain on the fault. Here, we present an alternative probabilistic model, built on the long-term fault memory model framework that better reflects the strain accumulation and release process. This generalized long-term fault memory model (GLTFM) predicts that this interevent time variability arises from both chance and the state of strain on the fault. Specifically, it estimates when residual strain is likely present and its impact on the timing of the next earthquake in the sequence. In addition, GLTFM assumes that additional accumulated strain always increases earthquake probability. In contrast, the commonly used lognormal and Brownian passage time models predict that the probability of a large earthquake stays constant or even decreases after it is “overdue” (past the observed average recurrence interval) so additional accumulated strain does not make an earthquake more likely. GLTFM’s simple implementation and versatility should make it a powerful tool in earthquake forecasting.

Integration of B-to-B trade network models of structural evolution and monetary flows reproducing all major empirical laws

We develop a single two-layered model framework that captures and replicates both the statistical properties of the network as well as those of the intrinsic quantities of the agents. Our model framework consists of two distinct yet connected elements that were previously only studied in isolation, namely methods related to temporal network structures and those associated with money transport flows. Within this context, the network structure emerges from the first layer and its topological structure is transferred to the second layer associated with the money transactions. In this manner, we can explain how the micro-level dynamics of the agents within the network lead to the exogenous manifestation of the aggregated system statistical data en-wrapping the very same agents within the system. This is done by capturing the essential dynamics of collective motion in complex networks that enable the simultaneous emergence of tent-shaped distributions in growth rates within the agents, together with the emergence of scaling properties within the network in the study. We can validate the model framework and dynamics by applying these to the context of the real-world inter-firm trading network of firms in Japan and comparing the results of the statistical distributions at both network and agent levels in a temporal manner. In particular, we compare our results to the fundamental quantities supporting the seven empirical laws observed in data: the degree distribution, the mean degree growth rate over time, the age distribution of the firms, the preferential attachment, the sales distribution in steady states, their growth rates, their scaling relations generated by the model. We find these results to be nearly identical to the real-world data. The framework has the potential to be transformed into a forecasting tool to support decision-makers on financial and prudential policies.

Comparison of Prediction Effectiveness in Deep Learning Perspective of China’s Data Finance

The study has developed a financial time series forecasting model, the Transformer-Encoder model, which utilizes the attention mechanism. This model has been applied to predict the closing price of the Shanghai Stock Exchange (SSE) index, a reliable indicator of financial trends. Furthermore, the study has conducted a comparative analysis, evaluating the performance of our model against other deep learning models, machine learning models, and traditional time series data forecasting models across short, medium, and long-term forecasting periods. Our study has yielded the following key findings: Firstly, the Transformer-Encoder model, leveraging the attention mechanism, demonstrates strong performance in predicting closing prices across short, medium, and long-term periods. This indicates the model’s viability in handling non-stationary financial data and its potential as a forecasting tool applicable to time series prediction problems within the economic sphere. Secondly, compared to alternative deep learning models, machine learning models, and traditional time series data forecasting models, our proposed model consistently outperforms them.

Assessment of the energy saving potential by regions of Ukraine (methodology and predictive assessment)

The purpose of the research is the develop methods and tools for long-term forecasting of electricity consumption based on the improved comprehensive method of demand for energy resources, taking into account the peculiarities of electricity use in the regions of Ukraine and their impact on the country's electricity consumption. The scientific novelty of the obtained results is the consideration of the technological potential of regional electricity conservation by means of electricity conservation measures typical for the regions. Thus, a complex method, a mathematical model, and a methodology for determining the forecast demand for electricity for three hierarchical levels of the economy were further developed. In these levels, the following groups of indicators are distinguished for forecasting electricity consumption: country, region, local level, or types of economic activity in the region. The formed three-level model (level I, level II, level III) takes into account both the general potential of electricity conservation (from structural and technological changes) in the country as a whole, and peculiarities at the regional level (region, city, village, settlement, territorial community) in certain (selected) types of economic activity in production (service provision). In the course of the work, material was collected on energy-saving measures in energy-intensive industries and forecasts of the development of certain types of production and service provision. These estimates determined the technological potential of energy saving in the regions. The 12 regions with the largest electricity consumption are highlighted, and the other regions are combined. In the region, the potential of electricity saving was distributed according to the structure of electricity consumption according to the types of economic activity inherent in this region. The technological potential of electricity conservation in the country will reach 10.35 billion kWh. Keywords: region, electricity-saving, electricity-saving potential, forecasting, energy efficiency, electricity.

A forecasting tool for a hospital to plan inbound transfers of COVID-19 patients from other regions

BackgroundIn April 2021, the province of Ontario, Canada, was at the peak of its third wave of the COVID-19 pandemic. Intensive Care Unit (ICU) capacity in the Toronto metropolitan area was insufficient to handle local COVID patients. As a result, some patients from the Toronto metropolitan area were transferred to other regions.MethodsA spreadsheet-based Monte Carlo simulation tool was built to help a large tertiary hospital plan and make informed decisions about the number of transfer patients it could accept from other hospitals. The model was implemented in Microsoft Excel to enable it to be widely distributed and easily used. The model estimates the probability that each ward will be overcapacity and percentiles of utilization daily for a one-week planning horizon.ResultsThe model was used from May 2021 to February 2022 to support decisions about the ability to accept transfers from other hospitals. The model was also used to ensure adequate inpatient bed capacity and human resources in response to various COVID-related scenarios, such as changes in hospital admission rates, managing the impact of intra-hospital outbreaks and balancing the COVID response with planned hospital activity.ConclusionsCoordination between hospitals was necessary due to the high stress on the health care system. A simple planning tool can help to understand the impact of patient transfers on capacity utilization and improve the confidence of hospital leaders when making transfer decisions. The model was also helpful in investigating other operational scenarios and may be helpful when preparing for future outbreaks or public health emergencies.

Modelling the industrial production of electric and gas utilities through the CIR3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$CIR^3$$\\end{document} model

This work aims to extend previous research on how a trifactorial stochastic model, which we call CIR3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$CIR^3$$\\end{document}, can be turned into a forecasting tool for energy time series. In particular, in this work, we intend to predict changes in the industrial production of electric and gas utilities. The model accounts for several stylized facts such as the mean reversion of both the process and its volatility to a short-run mean, non-normality, autocorrelation, cluster volatility and fat tails. In addition to that, we provide two theoretical results which are of particular importance in modelling and simulations. The first is the proof of existence and uniqueness of the solution to the SDEs system that describes the model. The second theoretical result is to convert, by the means of Lamperti transformations, the correlated system into an uncorrelated one. The forecasting performance is tested against an ARIMA-GARCH and a nonlinear regression model (NRM).

DNI Forecast Tool for the Smart Operation of a Parabolic Trough Collector System With Concrete Thermal Energy Storage

This work presents a basic forecast tool for predicting direct normal irradiance (DNI) in hourly resolution, which the Solar-Institut Jülich (SIJ) is developing within a research project. The DNI forecast data shall be used for a parabolic trough collector (PTC) system with a concrete thermal energy storage (C-TES) located at the company KEAN Soft Drinks Ltd in Limassol, Cyprus. On a daily basis, 24-hour DNI prediction data in hourly resolution shall be automatically produced using free or very low-cost weather forecast data as input. The purpose of the DNI forecast tool is to automatically transfer the DNI forecast data on a daily basis to a main control unit (MCU). The MCU automatically makes a smart decision on the operation mode of the PTC system such as steam production mode and/or C-TES charging mode. The DNI forecast tool was evaluated using historical data of measured DNI from an on-site weather station, which was compared to the DNI forecast data. The DNI forecast tool was tested using data from 56 days between January and March 2022, which included days with a strong variation in DNI due to cloud passages. For the evaluation of the DNI forecast reliability, three categories were created and the forecast data was sorted accordingly. The result was that the DNI forecast tool has a reliability of 71.4 % based on the tested days. The result fulfils SIJ’s aim to achieve a reliability of around 70 %, but SIJ aims to still improve the DNI forecast quality.

Verification of the Global Forecast System, North American Mesoscale Forecast System, and High-Resolution Rapid Refresh Model Near-Surface Forecasts by Use of the New York State Mesonet

Abstract Global Forecast System (GFS), North American Mesoscale Forecast System (NAM), and High-Resolution Rapid Refresh (HRRR) 2-m temperature, 10-m wind speed, and precipitation accumulation forecasts initialized at 1200 UTC are verified against New York State Mesonet (NYSM) observations from 1 January 2018 through 31 December 2021. NYSM observations at 126 site locations are used to calculate standard error statistics (e.g., forecast error, root-mean-square error) for temperature and wind speed and contingency table statistics for precipitation across forecast hours, meteorological seasons, and regions. The majority of the focus is placed on the first 18 forecast hours to allow for comparison among all three models. A daily NYSM station-mean temperature error analysis identified a slight cold bias at temperatures below 25°C in the GFS, a cool-to-warm bias as forecast temperatures warm in the HRRR, and a warm bias at temperatures above 30°C in each model. Differences arise when considering temperature biases with respect to lead times and seasons. Wind speeds are overforecast at all ranges in each season, and forecast wind speeds ≥ 18 m s−1 are rarely observed. Performance diagrams indicate overall good forecast performance at precipitation thresholds of 0.1–1.5 mm, but with a high frequency bias in the GFS and NAM. This paper provides an overview of deterministic forecast performance across New York State, with the aim of sharing common biases associated with temperature, wind speed, and precipitation with operational forecasters and is the first step in developing a real-time model forecast uncertainty prediction tool.

Internet publishing as a forecasting tool in the crypto market

Subject. This article examines the relationship between the sentiment caused by the news on the CoinTelegragh professional forum and the changes in Bitcoin, Litecoin and Ethereum cryptocurrencies. Objectives. The article aims to assess the impact of the sentiment of various Internet publications on the volatility of cryptocurrencies, as well as the predictive power of Google Trends and the VIX Index for cryptocurrencies. Methods. For the study, we used the cross-quantilogram method and the VADER sentiment analysis model. Results. The article finds that the Google Trends Index in a short period of one to three days can be used to predict the closing prices of Bitcoin, Litecoin, and Ethereum, while the VIX Index (Stock Market Uncertainty) has no relationship with the cryptocurrency market. This means that cryptocurrencies can be used as a safe-haven asset when the background market is highly volatile. Conclusions. The crypto market has a complex sentiment component, with its prices and trading activity determined by popularity, emotion, and sentiment. The findings confirm previous studies, which claim that during the period of prevalence of negative news and publications, the crypto market gets narrowed, the trading volume drops off, and the interest of Internet users gets low to a minimum. The euphoria in the market, on the contrary, attracts new unqualified investors, and this is confirmed by the number of views of basic information about cryptocurrencies on Wikipedia.

Developing a Supervisory Controller for Hybrid Power System: Fernando de Noronha Island Case

This paper presents the development of a supervisory controller and the main challenges of integrating wind and solar energy into an existing grid with 100% diesel generation. The supervisory controller has been developed for keeping the safety and stability of the network a R&D project financed by the Utility CELPE. The controller keeps the balance of power at all the time and monitors the stability of the network. The controller reads the power production from all sources and define the load in the network and it does two main tasks: the first is to evaluate the stability of the power system; the second is to estimate the diesel production for the next 2 hours. The diesel dispatch is done based on the power and load forecasts. The main features of the supervisory are first of all implemented using a open source software Scada and using the Matlab ® as the main calculus processor, secondly the controller is flexible to operate with different communication protocols, to work with different forecast tools and to act as desired by the power system operator. Actually the power system operator does not accept the automatic control of the diesel units on the other hand, the renewable sources should be automatic controlled, however this feature is flexible in the supervisory controller.