Energy Production Forecast Research Articles

Нейросетевая модель для краткосрочного прогнозирования выработки электрической энергии солнечными электростанциями

Today, energy consumption in the world is growing and it is becoming urgent to solve the problem of replacing traditional energy sources with alternative ones. The solution to this problem is impossible without a preliminary data analysis and further forecasting of energy production by alternative sources. However, the use of alternative energy sources in the conditions of the wholesale electricity and capacity market currently operating on the territory of the Russian Federation is impossible without the use of short-term predictive “day ahead” models. In this article, the authors perform a brief analysis of the existing methods of short-term forecasting which are used when making forecasts for the generation of electricity by solar power plants. Currently, there are already a fairly large number of predictive models built within each of the selected methods of short-term forecasting, and they all differ in their characteristics. Therefore, in order to identify the most promising method of short-term forecasting for further use and development, the authors used a previously developed classification. In the course of the study, a preliminary processing of initial data obtained from the existing solar power plants using spectral analysis was carried out. Further, to build a predictive model, a correlation analysis of the initial data was carried out, which showed the absence of a linear relationship between the components in the retrospective data. Based on the results of the correlation analysis the authors made a decision to select parameters empirically in order to build a predictive model. As a result of the study, a mathematical model based on an artificial neural network was proposed and a learning sample was generated for it. In addition, the architecture of an artificial neural network was determined, the result of which is a short-term forecast of electric power generation in the "day ahead" mode, and calculations were performed to obtain numerical values of the forecast. From the results of the study, it follows that the developed predictive model in the predicted interval has a mean absolute error of about 13.5 MW. However, at some intervals, the peak discrepancies can reach up to 200 MW. The root mean square error of the model is 27.8 MW.

Economic Energy Efficiency of Food Production Systems

The current global population growth forecast carries with it a global increase in demand for food. In order to meet this demand, it is necessary to increase production, which requires an increase in energy consumption. However, forecasted energy production growth is insufficient and traditional sources of energy are limited; hence, it is necessary to strive for greater energy efficiency in food production systems. The study aimed to compare the economic energy efficiency of food production systems in selected countries and identify the sources of diversification in this field. As a measure of energy efficiency, the indicators of the energy intensity of food production were used in this study. To calculate these indicators, a method based on input-output life-cycle assessment assumptions was used, which enables researchers to obtain fully comparable results between countries. The study showed that despite an increase in energy consumption in the food production systems of the analyzed countries by an average of 27%, from 19.3 EJ to 24.5 EJ, from 2000 to 2014, their energy intensity decreased, on average, by more than 18%, from 8.5 MJ/USD to 6.9 MJ/USD. This means that energy efficiency improvements are possible even under conditions of increased energy consumption, which in turn, means that food production can increase significantly. In the case of developed countries, the main inefficiencies are found in agricultural production, while in developing countries, they are observed in the food industry. Decision-makers should also pay attention to the high level of energy intensity that results from the supply of inputs to agriculture and the food industry because there is great potential for the improvement of energy efficiency in this field, especially because energy consumption associated with supply constitutes a major part of total consumption in the food production systems of developed countries.

Rooftop Photovoltaic Energy Production Management in India Using Earth-Observation Data and Modeling Techniques

This study estimates the photovoltaic (PV) energy production from the rooftop solar plant of the National Institute of Technology Karnataka (NITK) and the impact of clouds and aerosols on the PV energy production based on earth observation (EO)-related techniques and solar resource modeling. The post-processed satellite remote sensing observations from the INSAT-3D have been used in combination with Copernicus Atmosphere Monitoring Service (CAMS) 1-day forecasts to perform the Indian Solar Irradiance Operational System (INSIOS) simulations. NITK experiences cloudy conditions for a major part of the year that attenuates the solar irradiance available for PV energy production and the aerosols cause performance issues in the PV installations and maintenance. The proposed methodology employs cloud optical thickness (COT) and aerosol optical depth (AOD) to perform the INSIOS simulations and quantify the impact of clouds and aerosols on solar energy potential, quarter-hourly monitoring, forecasting energy production and financial analysis. The irradiance forecast accuracy was evaluated for 15 min, monthly, and seasonal time horizons, and the correlation was found to be 0.82 with most of the percentage difference within 25% for clear-sky conditions. For cloudy conditions, 27% of cases were found to be within ±50% difference of the percentage difference between the INSIOS and silicon irradiance sensor (SIS) irradiance and it was 60% for clear-sky conditions. The proposed methodology is operationally ready and is able to support the rooftop PV energy production management by providing solar irradiance simulations and realistic energy production estimations.

Forecasting the Structure of Energy Production from Renewable Energy Sources and Biofuels in Poland

The world’s economic development depends on access to cheap energy sources. So far, energy has been obtained mainly from conventional sources like coal, gas and oil. Negative climate changes related to the high emissions of the economy based on the combustion of hydrocarbons and the growing public awareness have made it necessary to look for new ecological energy sources. This condition can be met by renewable energy sources. Both social pressure and international activities force changes in the structure of sources from which energy is produced. This also applies to the European Union countries, including Poland. There are no scientific studies in the area of forecasting energy production from renewable energy sources for Poland. Therefore, it is reasonable to investigate this subject since such a forecast can have a significant impact on investment decisions in the energy sector. At the same time, it must be as reliable as possible. That is why a modern method was used for this purpose, which undoubtedly involves artificial neural networks. The following article presents the results of the analysis of energy production from renewable energy sources in Poland and the forecasts for this production until 2025. Artificial neural networks were used to make the forecast. The analysis covered eight main sources from which this energy is produced in Poland. Based on the production volume since 1990, predicted volumes of renewable energy sources until 2025 were determined. These forecasts were prepared for all studied renewable energy sources. Renewable energy production plans and their share in total energy consumption in Poland were also examined and included in climate plans. The research was carried out using artificial neural networks. The results should be an important source of information on the effects of implementing climate policies in Poland. They should also be utilized to develop action plans to achieve the objectives of the European Green Deal strategy.

A neural network model for short-term PV - energy forecasting

Nowadays, energy consumption in the world is growing becomes relevant to solve the problem of replacing traditional sources with alternative ones. The solution to this problem is impossible without preliminary forecasting of energy production by alternative sources. In this paper, we consider the problem of solving the problem forecasting electric energy by solar power plants, considering the influence of external factors (weather conditions) using a model implemented on the basis a neural network.

An Induction Curve Model for Prediction of Power Output of Wind Turbines in Complex Conditions

Power generation from wind farms is traditionally modeled using power curves. These models are used for assessment of wind resources or for forecasting energy production from existing wind farms. However, prediction of power using power curves is not accurate since power curves are based on ideal uniform inflow wind, which do not apply to wind turbines installed in complex and heterogeneous terrains and in wind farms. Therefore, there is a need for new models that account for the effect of non-ideal operating conditions. In this work, we propose a model for effective axial induction factor of wind turbines that can be used for power prediction. The proposed model is tested and compared to traditional power curve for a 2.5 MW horizontal axis wind turbine. Data from supervisory control and data acquisition (SCADA) system along with wind speed measurements from a nacelle-mounted sonic anemometer and turbulence measurements from a nearby meteorological tower are used in the models. The results for a period of four months showed an improvement of 51% in power prediction accuracy, compared to the standard power curve.

On the wake effect in wind farm power forecasting: a new data-driven approach

Wind power generation differs from other energy sources, such as thermal, solar or hydro, due to the inherent stochastic nature of wind. For this reason wind power forecasting, especially for wind farms, is a complex task that cannot be accurately solved with traditional statistical methods or needs large computational systems if physical models are used. Recently, the so-called learning approaches are considered a good compromise among the previous methods since they are able to integrate physical phenomena such as wake effects without presenting heavy computational loads. The present work deals with an innovative method to forecast wind power generation in a wind farm with a combination of GISbased methods, neural network approach and a wake physical model. This innovative method was tested with a wind farm located in Sicily (Italy), used as a case study. It consists of 30 identical wind turbines (850 kW each one), located at different heights, for an overall Power peak of 25 MW. The time series dataset consists of one year with a sampling time of 10 minutes considering wind speeds and wind directions. The output of this innovative model leaded to good results, especially for medium-term overall energy production forecast for the case study.

Review and Comparison of Intelligent Optimization Modelling Techniques for Energy Forecasting and Condition-Based Maintenance in PV Plants

Within the field of soft computing, intelligent optimization modelling techniques include various major techniques in artificial intelligence. These techniques pretend to generate new business knowledge transforming sets of "raw data" into business value. One of the principal applications of these techniques is related to the design of predictive analytics for the improvement of advanced CBM (condition-based maintenance) strategies and energy production forecasting. These advanced techniques can be used to transform control system data, operational data and maintenance event data to failure diagnostic and prognostic knowledge and, ultimately, to derive expected energy generation. One of the systems where these techniques can be applied with massive potential impact are the legacy monitoring systems existing in solar PV energy generation plants. These systems produce a great amount of data over time, while at the same time they demand an important effort in order to increase their performance through the use of more accurate predictive analytics to reduce production losses having a direct impact on ROI. How to choose the most suitable techniques to apply is one of the problems to address. This paper presents a review and a comparative analysis of six intelligent optimization modelling techniques, which have been applied on a PV plant case study, using the energy production forecast as the decision variable. The methodology proposed not only pretends to elicit the most accurate solution but also validates the results, in comparison with the different outputs for the different techniques.

Trust-less electricity consumption optimization in local energy communities

Optimizing energy consumption in local energy communities is one of the key contributions to the so-called smart grid. Such communities are equipped with rooftop photovoltaic power plants or other forms of small power plants for local energy production. In addition, a number of appliances allow for shiftable energy consumption, e.g., heat pumps or electric vehicle charging stations. The ability to shift is, however, dependent on customer preferences. In this paper, we present a trust-less approach for optimizing the electricity consumption in a local energy community given forecasts of energy production and customer demands, along with constraints for shiftable loads. In larger communities, appointing a single party for managing load curtailment requires a high level of trust. In the proposed trust-less approach, all parties can independently propose optimal solutions for this optimization problem and then globally agree one one solution that meets the defined requirements to the greatest extent.

ENERGY EFFICIENT DEVELOPMENT MODEL FOR REGIONS OF THE RUSSIAN FEDERATION: EVIDENCE OF CRYPTO MINING

Paper proposes a model of energy efficient development of the regional energy complex, in which provided by mixing and iterative matching forecasts of energy consumption and energy production. The novelty of this method is that it formed in the framework of the regional model, is part of the overall regional product-sector balance formed for the region, which allows to simulate the mutual influence of energy complex and the rest of the economy through interbalance relations. Search for optimal solutions are carried out by iteration using a specially designed approach based on the Stiglitz-Sen-Fitoussi approach. It includes the following welfare components: economic well-being, non-monetary characteristic welfare and reproduction potential of the economy. System of crypto mining based on the evidence of proof-of-work protocol are extremely no energy efficient. Paper also gives evidence of Russian regions for mining of cryptocurrencies in Russia.Keywords: energy, resource saving, energy efficient development, energy indicators, forecastingJEL Classifications: C30, D12, Q41, Q48DOI: https://doi.org/10.32479/ijeep.7759

Analysis of possibilities to improve quality of spatial wind speed forecasts for efficient forecasting of electric energy production in onshore wind farms in Poland

The most important factor responsible for the quality of energy production forecasts in wind farms is the accurate wind speed forecast. An extensive statistical analysis of meteorological data (NWP) from 16 base nodes of the "300" grid in the "Łódź" area was made. The intention of the statistical analysis was to select potential explanatory variables for models predicting wind speed in the remaining 206 nodes of the grid’s mesh. Next, tests of selected prognostic methods were performed in order to compare their effectiveness with bilinear method which is not computationally complex. It should be emphasized that the main problem in spatial wind speed forecasting is the very large number of nodes for which the forecasts are calculated. As a consequence, more advanced and computationally complex forecasting methods cannot be applied in practice due to too long calculations time and difficulties in huge amounts of data processing. Conclusions with proposals of preferred forecasting methods that could be used in practice were developed.

ОСОБЛИВОСТІ РИНКУ БІОЕНЕРГЕТИКИ УКРАЇНИ

Introduction. Ukraine has a number of problems with ensuring the economy and population with traditionalenergy resources, especially oil and gas, imported by one country - Russia. Energy, industrial production and roadtransport in the country are significant environmental pollutants due to the use of outdated technologies. In addition,the phenomena that violate the civilized life cycle continue to develop in the world - traditional sources of energy areexhausted, their cost of extraction increases, the environment is intensively polluted, the biosphere is destroyed, anexcessive amount of organic waste from industrial, agricultural and domestic origin is generated. Elimination of allthese problems should be carried out at an accelerated pace, therefore the necessity of development of bioenergetics inUkraine is unmistakable - it is an option that has a global perspective for the further successful development ofcivilization.Purpose. The research aims at outline the current state of the bioenergy market in Ukraine and to substantiatethe prospects for its development.Results. The research substantiates the concept and fundamental peculiarities of bioenergetics as an alternativeenergy industry. Based on the analysis of the structure of energy production, total energy supply and energyconsumption on the basis of renewable sources in Ukraine, strategic directions for the development of the renewableenergy sector are identified. The steady development of the bioenergy market was proven, as well as a new detailedanalysis of the production of alternative energy, the forecast of alternative energy production in Ukraine was made.The existing bioenergy potential of Ukraine is analyzed. According to the Energy Strategy of Ukraine until 2035, thepriorities of development in the field of bioenergy are determined.Originality. The structure of energy production in Ukraine, the structure of general energy supply in Ukraine,the energy consumption on the basis of renewable sources in Ukraine, and the forecast of alternative energy productionin Ukraine up to 2027 are analyzed in detail. The forecast of the development of bioenergy as a real alternative totraditional energy is justified considering the real possibilities and perspectives of the development of Ukraine'sbioenergy potential.Conclusion. For Ukraine, bioenergy is one of the strategic directions for the development of the renewableenergy sector, given the high dependence of the country on imported energy resources, first of all, on natural gas, andthe high potential of biomass available for energy production. Unfortunately, the pace of bioenergy development inUkraine still lags far behind European ones. Although in recent years there has been a significant shift in Ukraine andbioenergy has begun ˗ what is worth only 1.8 GW of new capacities in 2017 ˗ but this is not enough to reach theplanned level, according to the Energy Strategy of Ukraine until 2035. But despite quite ambitious goals of theNational RE and Energy Strategy are quite realistic, given the forecast. This is evidenced by the estimates of theBioenergy Association of Ukraine on biomass resources available for energy use. The country has an availablebiomass of over 21 million tons. e. per year with a fixed need of 11 million tons n. e. per year. But taking into accountonly the potential and the availability of sufficient biomass in Ukraine is not enough, further promotion of bioenergydevelopment by the government and parliament is necessary, otherwise these plans will remain on paper.

Real-Time Congestion Management in Distribution Networks by Flexible Demand Swap

In addition to the day-ahead congestion management in distribution networks, the real-time congestion management is very important because many unforeseen events can occur at the real operation time, e.g., loss of generation of distributed energy resources or inaccurate forecast of energy consumption or production. Flexibility service from demand will be a good option to solve the real-time congestions if the cost of activating the flexibility service is fully addressed. This paper proposes a new method, namely “swap,” to employ the flexibility service from electric vehicles and heat pumps for real time congestion management. The swap method can maintain the power balance of the system and avoid the imbalance cost of activating the flexibility service. An algorithm for forming swaps through optimal power flow and mixed integer linear programming is proposed to implement the swap method. Case studies were carried out to validate the efficacy of the proposed swap method for real time congestion management and the proposed algorithm for forming swaps. The settlement process for the swaps in different markets is analyzed.

Comparison of the existing photovoltaic power plant performance simulation in terms of different sources of meteorological data

Detailed and reliable design of a photovoltaic (PV) power plant cannot be underestimated. In terms of energy production forecasts and legal issues concerning financing - simulation performance of the PV installation is a very important problem for both the investor and the bank. In order to simulate the performance of the PV system one has to choose proper meteorological database which would be the most reliable on the installation site. Since different meteorological databases have different measurements methods and analysis, choosing the best one is not a trivial problem. In order to find the best meteorological data source for PV simulation performance under eastern Poland conditions, PVSYST software has been used. Detailed project of an 1.39 MWp PV power plant located in Bordzilowka in the east of Poland was introduced into the software. Choosing different meteorological data bases, monthly and yearly energy production was established. The simulation results were compared with the real data obtained from the PV power plant.

Optimal Power Consumption in Micro Grid Based on PV Array

Microgrids provide the ability to take energy from the grid as well as feed power directly to the grid through low voltage networks, thereby allowing the customer to become an active participant in the grid. They should also be able to work in emergency state and be disconnected from the grid, called islanded mode. Prediction and forecasting of renewable energy production plays a key role in microgrid management and control. Prediction algorithms are implemented part of the Energy Information Algorithm of microgrids. In smart microgrid is coupled with power system to deliver a smart system that can provide energy in efficient manner. Energy information system plays, therefore, a key role in managing the resources within the microgrid and can be thought as a layer on the top of the power layer.

Ideal Operation of a Photovoltaic Power Plant Equipped with an Energy Storage System on Electricity Market

There is no natural inertia in a photovoltaic (PV) generator and changes in irradiation can be seen immediately at the output power. Moving cloud shadows are the dominant reason for fast PV power fluctuations taking place typically within a minute between 20 to 100% of the clear sky value roughly 100 times a day, on average. Therefore, operating a utility scale grid connected PV power plant is challenging. Currently, in many regions, renewable energy sources such as solar and wind receive feed-in tariffs that ensure a certain price for the energy. On the other hand, electricity markets operate on a supply-demand principle and a typical imbalance settlement period is one hour. This paper presents the energy, power and corresponding requirements for an energy storage system in a solar PV power plant to feed the power to the grid meeting the electricity spot markets practices. An ideal PV energy production forecast is assumed to be available to define reference powers of the system for the studied imbalance settlement periods. The analysis is done for three different PV system sizes using the existing irradiance measurements of the Tampere University of Technology solar PV power station research plant.

Prediction in Photovoltaic Power by Neural Networks

The ability to forecast the power produced by renewable energy plants in the short and middle term is a key issue to allow a high-level penetration of the distributed generation into the grid infrastructure. Forecasting energy production is mandatory for dispatching and distribution issues, at the transmission system operator level, as well as the electrical distributor and power system operator levels. In this paper, we present three techniques based on neural and fuzzy neural networks, namely the radial basis function, the adaptive neuro-fuzzy inference system and the higher-order neuro-fuzzy inference system, which are well suited to predict data sequences stemming from real-world applications. The preliminary results concerning the prediction of the power generated by a large-scale photovoltaic plant in Italy confirm the reliability and accuracy of the proposed approaches.

Predictive Models for Photovoltaic Electricity Production in Hot Weather Conditions

The process of finding a correct forecast equation for photovoltaic electricity production from renewable sources is an important matter, since knowing the factors affecting the increase in the proportion of renewable energy production and reducing the cost of the product has economic and scientific benefits. This paper proposes a mathematical model for forecasting energy production in photovoltaic (PV) panels based on a self-organizing feature map (SOFM) model. The proposed model is compared with other models, including the multi-layer perceptron (MLP) and support vector machine (SVM) models. Moreover, a mathematical model based on a polynomial function for fitting the desired output is proposed. Different practical measurement methods are used to validate the findings of the proposed neural and mathematical models such as mean square error (MSE), mean absolute error (MAE), correlation (R), and coefficient of determination (R2). The proposed SOFM model achieved a final MSE of 0.0007 in the training phase and 0.0005 in the cross-validation phase. In contrast, the SVM model resulted in a small MSE value equal to 0.0058, while the MLP model achieved a final MSE of 0.026 with a correlation coefficient of 0.9989, which indicates a strong relationship between input and output variables. The proposed SOFM model closely fits the desired results based on the R2 value, which is equal to 0.9555. Finally, the comparison results of MAE for the three models show that the SOFM model achieved a best result of 0.36156, whereas the SVM and MLP models yielded 4.53761 and 3.63927, respectively. A small MAE value indicates that the output of the SOFM model closely fits the actual results and predicts the desired output.

Optimization Models for Islanded Micro-Grids: A Comparative Analysis between Linear Programming and Mixed Integer Programming

This paper presents a comparison of optimization methods applied to islanded micro-grids including renewable energy sources, diesel generators and battery energy storage systems. In particular, a comparative analysis between an optimization model based on linear programming and a model based on mixed integer programming has been carried out. The general formulation of these models has been presented and applied to a real case study micro-grid installed in Somalia. The case study is an islanded micro-grid supplying the city of Garowe by means of a hybrid power plant, consisting of diesel generators, photovoltaic systems and batteries. In both models the optimization is based on load demand and renewable energy production forecast. The optimized control of the battery state of charge, of the spinning reserve and diesel generators allows harvesting as much renewable power as possible or to minimize the use of fossil fuels in energy production.

Cost-optimal Control of Photovoltaic Systems with Battery Storage under Variable Electricity Tariffs

In this paper, domestic homes with a photovoltaic system, a battery storage and variable electricity tariffs are considered. First, a control oriented model of the system is developed. Second, three controllers are designed with the aim to minimize costs for the user, and at the same time help the energy provider maintaining grid stability, by proper battery load management. A base-level controller serves as performance reference for the advanced optimization based controllers (a nonlinear controller with full system knowledge and a linear controller) which utilize forecasts of energy production and consumption. Performance evaluation was done in simulation using real-world measurement data over a full year. Results show that significant cost reductions can be achieved with the nonlinear controller while the linear one suffers from imprecise system knowledge.