Electric load analysis using an artificial neural network

Load forecasting in the current, increasingly liberalized, electricity power market is of crucial importance as a means for producers to optimize and rationalize energy supply. A number of electric power companies are equipped to make forecasts with the aid of traditional statistical methods. This paper presents the use of an artificial neural net to an hourly based load forecasting application for a small electric grid on an Italian island (Lipari) not connected to the mainland. The aim was to examine the forecasting ability of a neural net in a situation where the electric load was subject to considerable seasonal variations over the year. The variations are affected by energy demand related to the tourism season as well as by climatic conditions, especially temperature. The network developed was a multi‐layer perceptron type built on three layers trained with a back‐propagation algorithm. The input layer receives all the most relevant information regarding: the class of day type, the hour in the daytime, the load and background temperature recorded at the indicated time for the months of March, August and October whilst the output layer provides the forecast value at the indicated time in December. The results obtained are encouraging; in the training phase the RMS error rate was around 2% and this rate settled at about 2.6% during testing. As both the margins of error recorded are acceptable, the use of a neural network for electric load forecasting applications can be considered an attractive option. Copyright © 2005 John Wiley & Sons, Ltd.     

Thermodynamic analysis of absorption systems using artificial neural network

Thermodynamic analysis of absorption systems is a very complex process, mainly because of the limited experimental data and analytical functions required for calculating the thermodynamic properties of fluid pairs, which usually involves the solution of complex differential equations. In order to simplify this complex process, Artificial Neural Networks (ANNs) are used. In this study, ANNs are used as a new approach for the determination of the thermodynamic properties of LiBr–water and LiCl–water solutions which have been the most widely used in the absorption heat pump systems. Instead of complex differential equations and limited experimental data, faster and simpler solutions were obtained by using equations derived from the ANN model. It was found that the coefficient of multiple determination (R²-value) between the actual and ANN predicted data is equal to about 0.999 for the enthalpy of both LiBr–water and LiCl–water solutions. As seen from the results obtained, the calculated thermodynamic properties are obviously within acceptable limits. In addition, the coefficient of performance (COP) of absorption systems operating under different conditions with LiBr–water and LiCl–water solutions is calculated. The use of the derived equations, which can be employed with any programming language or spreadsheet program for the estimation of the enthalpy of the solutions, as described in this paper, may make the use of dedicated ANN software unnecessary.     

Synchronous machine parameter estimation using the Hartley series

This paper presents a novel alternative to estimate armature circuit parameters of large utility generators using real time operating data. The proposed approach uses the Hartley series for fitting operating data (voltage and currents measurements). The essence of the method is the use of linear state estimation to identify the coefficients of the Hartley series. The approach is tested for noise corruption likely to be found in measurements. The method is found to be suitable for the processing of digital fault recorder data to identify synchronous machine parameters     

Artificial neural network analysis of heat pumps using refrigerant mixtures

In this study, we have investigated the performance of a vapor compression heat pump with different ratios of R12/R22 refrigerant mixtures using artificial neural networks (ANN). Experimental studies were completed to obtain training and test data. Mixing ratio, evaporator inlet temperature and condenser pressure were used as input layer, while the outputs are coefficient of performance (COP) and rational efficiency (RE). The back propagation learning algorithm with three different variants and logistic sigmoid transfer function were used in the network. It is shown that the R² values are about 0.9999 and the RMS errors are smaller than 0.006. With these results, we believe that the ANN can be used for prediction of COP and RE as an accurate method in a heat pump.     

Synchronous Machine steady-State parameter estimation using neural networks

An online steady-state parameter estimation technique using the ability of the neural networks to recognize patterns is presented in this paper. The method is nonintrusive. Studies on a salient pole and on a round rotor synchronous machine illustrate the effectiveness of the proposed technique. Results indicate that the steady-state parameters can be obtained without the use of rotor position.     

A new lumped steady-state synchronous machine model derived fromfinite element analysis

A new steady-state model describes synchronous reactance over the whole operating region of the machine. The model takes into account main and cross-axis magnetic saturation. An analysis is presented of saturation as a function of the total air gap ampere-turns which shows that complicated relationships exist at leading power factors making such relationships unuseful. Two large turbogenerators of 13.8 kV, 150 MVA and 22 kV, 588 MVA are studied     

Exergy analysis of an ejector-absorption heat transformer using artificial neural network approach

This paper proposes artificial neural networks (ANNs) technique as a new approach to determine the exergy losses of an ejector-absorption heat transformer (EAHT). Thermodynamic analysis of the EAHT is too complex due to complex differential equations and complex simulations programs. ANN technique facilitates these complicated situations. This study is considered to be helpful in predicting the exergetic performance of components of an EAHT prior to its setting up in a thermal system where the working temperatures are known. The best approach was investigated using different algorithms with developed software. The best statistical coefficient of multiple determinations (R²-value) for training data equals to 0.999715, 0.995627, 0.999497, and 0.997648 obtained by different algorithms with seven neurons for the non-dimensional exergy losses of evaporator, generator, absorber and condenser, respectively. Similarly these values for testing data are 0.999774, 0.994039, 0.999613 and 0.99938, respectively. The results show that this approach has the advantages of computational speed, low cost for feasibility, rapid turnaround, which is especially important during iterative design phases, and easy of design by operators with little technical experience.     

Synchronous machine parameters estimation using a new genetic‐based algorithm

A new genetic‐based algorithm (GA) for estimating synchronous machine parameters from frequency tests is presented in this paper. GAs are general search techniques based on biological concepts and are very suitable for solving optimization problems. The proposed method uses a set of digital measurements for the direct axis impedance magnitude and phase as functions of frequency for estimating both the d‐ and q‐axis parameters, such as direct reactance and time constants. The problem is formulated as an optimization problem and solved using the proposed method. Two different models along with different fitness functions are suggested to be used with the genetic algorithm. A practical example from the literature is used to test the proposed algorithm. The results obtained are compared with those given in the literature using other methods. The results and comparison show that the new algorithm is very applicable and highly accurate. Copyright © 1999 John Wiley and Sons, Ltd.     

Electric load forecasting by using dynamic neural network

Electrical energy is fundamental for the wellbeing and for the economic development of any country. However, all countries must ensure access to essential resources and ensure the continuity of its supply. Due to the non-storable nature of electrical energy, the amount of consumed active power should always be equal the produced active power just to avoid power system frequency deviation problem. In order to keep the relationship production–consumption relation in compliance with different standards and to secure profitable operations of power system, electric load consumption must be predicted and controlled instantaneously. Several statistical and classical techniques are proposed in the literature but unfortunately all these methods are not accurate in a satisfactory manner. In this paper, a dynamic neural network is used for the prediction of daily power consumption. The suitability and the performance of the proposed approach is illustrated and verified with simulations on load data collected from French Transmission System Operator (RTE) website. The obtained results show that the accuracy and the efficiency are improved comparatively to conventional methods widely used in this field of research.     

Cost-optimal Calculation for Ice-Storage Systems Using Neural Network

Based on the eminent characteristics of the ice-storage systems, which can shift cooling electrical demand from peak time to off peak time, this paper describes the ice storage air-conditioning system that is now used much frequently. The authors develop the operating cost model by simplification and introduce a neural network model and try to solve the optimal cost problem of operation by using this neural network model. In calculation, any trajectory of the neural network converges to its solution in finite time, which is consistent with result by simplex method. Comparing with different methods, the neural network is more effective, which can be alternative to simplex method in calculating the optimal cost model for ice storage air-conditioning systems.     

Artificial neural network analysis of Moroccan solar potential

An artificial neural network (ANN) model is used to forecast the annual and monthly solar irradiation in Morocco. Solar irradiation data are taken from the new Satellite Application Facility on Climate Monitoring (CM-SAF)-PVGIS database. The database represents a total of 12 years of data from 1998 to 2010. In this paper, the data are inferred using an ANN algorithm to establish a forward/reverse correspondence between the longitude, latitude, elevation and solar irradiation. Specifically, for the ANN model, a three-layered, back-propagation standard ANN classifier is considered consisting of three layers: input, hidden and output layer. The learning set consists of the normalised longitude, latitude, elevation and the normalised mean annual and monthly solar irradiation of 41 Moroccan sites. The testing set consists of patterns just represented by the input component, while the output component is left unknown and its value results from the ANN algorithm for that specific input. The results are given in the form of the annual and monthly maps. They indicate that the method could be used by researchers or engineers to provide helpful information for decision makers in terms of sites selection, design and planning of new solar plants.     

Performance analysis of single-stage refrigeration system with internal heat exchanger using neural network and neuro-fuzzy

In this study, artificial neural networks (ANNs) and adaptive neuro-fuzzy (ANFIS) have been used for performance analysis of single-stage vapour compression refrigeration system with internal heat exchanger using refrigerants R134a, R404a, R407c which do not damage to ozone layer. It is well known that the evaporator temperature, condenser temperature, subcooling temperature, superheating temperature and cooling capacity affect the coefficient of performance (COP) of single-stage vapour compression refrigeration system with internal heat exchanger. In this study, COP is estimated depending on the above temperatures and cooling capacity values. The results of ANN are compared with ANFIS in which the same data sets are used. ANN model is slightly better than ANFIS for R134a whereas ANFIS model is slightly better than ANN for R404a and R407c. In addition, new formulations obtained from ANN for three refrigerants are presented for the calculation of the COP. The R² values obtained when unknown data were used to the networks were 1, 0.999998 and 0.999998 for the R134a, R404a and R407c respectively which is very satisfactory.     

Wind turbine power curve modelling using artificial neural network

Technical improvements over the past decade have increased the size and power output capacity of wind power plants. Small increases in power performance are now financially attractive to owners. For this reason, the need for more accurate evaluations of wind turbine power curves is increasing. New investigations are underway with the main objective of improving the precision of power curve modeling. Due to the non-linear relationship between the power output of a turbine and its primary and derived parameters, Artificial Neural Network (ANN) has proven to be well suited for power curve modelling. It has been shown that a multi-stage modelling techniques using multilayer perceptron with two layers of neurons was able to reduce the level of both the absolute and random error in comparison with IEC methods and other newly developed modelling techniques. This newly developed ANN modeling technique also demonstrated its ability to simultaneously handle more than two parameters. Wind turbine power curves with six parameters have been modelled successfully. The choice of the six parameters is crucial and has been selected amongst more than fifty parameters tested in term of variability in differences between observed and predicted power output. Further input parameters could be added as needed.     

Artificial neural network analysis of world green energy use

This paper focuses on the analysis of world green energy consumption through artificial neural networks (ANN). In addition, the consumption is also analyzed of world primary energy including fossil fuels such as coal, oil and natural gas. A feed-forward back-propagation ANN is used for training and learning processes by taking into consideration data from the literature of world energy consumption from 1965 to 2004. Also, an ANN approach for forecasting world green energy consumption to the year 2050 is presented, and the consumption equations for different energy sources are derived. The environmental aspects of green energy and fossil fuels are discussed in detail. The resulting ANN-based equation curve profiles verify that the available economic reserves of fossil fuel resources are limited, and become “depleted” in the near future. It is expected that world green energy consumption will reach almost 62.74 EJ by 2010, and be on average 32.29% of total energy use between 2005 and 2025. However, world green energy and natural gas consumption will continue increasing after 2050, while world oil and coal consumption are expected to remain relatively stable after 2025 and 2045, respectively. The ANN approach appears to be a suitable method for forecasting energy consumption data, should be utilized in efforts to model world energy consumption.     

Artificial neural network analysis of liquid desiccant dehumidification system

The dehumidification process involves simultaneous heat and mass transfer and reliable transfer coefficients are required in order to analyze the system. This has been proved to be difficult and many assumptions are made to simplify the analysis. The present research proposes the use of ANN based model in order to simulate the relationship between inlet and outlet parameters of the dehumidifier. For the analysis, randomly packed dehumidifier with lithium chloride as the liquid desiccant is chosen. A multilayer ANN is used to investigate the performance of dehumidifier. For training ANN models, data is obtained from analytical equations. Eight parameters are used as inputs to the ANN, namely: air and desiccant flow rates, air and desiccant inlet temperatures, air inlet humidity, desiccant inlet concentration, dimensionless temperature ratio, and inlet temperature of the cooling water. The outputs of the ANN are the water condensation rate and the outlet desiccant concentration as well as its temperature. ANN predictions for these parameters are validated well with experimental values available in the literature with R² value in the range of 0.9251-0.9660. This study shows that liquid desiccant dehumidification system can be alternatively modeled using ANN with a reasonable degree of accuracy.     

Synchronous machine parameters from sudden-short tests byback-solving

The calculation of synchronous machine parameters from sudden short-circuit measurements has been subject to unnecessary assumptions and approximations. The authors describe the nature of these approximations and a procedure to remove them, and illustrate how a new philosophy for problem formulation and solution using backsolving programs can simplify and clarify the solution process. They introduce a method of calculating eigenvalues that is based on a generalization of the eigenvalue problem. Approximations are unnecessary, and the formulation is much cleaner than in previous efforts. Even for large values of armature resistance, the time constants are correctly computed, since the coupling between axes is considered     

Modelling of a cascade refrigeration system using artificial neural network

This study investigates the applicability of artificial neural networks (ANNs) to predict various performance parameters of a cascade vapour compression refrigeration system. For this aim, an experimental cascade system was set up and tested in steady‐state operating conditions. Then, utilizing some of the experimental data for training, an ANN model for the system based on the standard back propagation algorithm was developed. The ANN was used for predicting the evaporating temperature in the lower‐temperature circuit, compressor power for the lower and higher circuits and coefficients of performance for both the lower circuit and the overall cascade system. Afterwards, the performances of the ANN predictions were tested using new experimental data. The ANN predictions usually agreed well with the experimental results with correlation coefficients in the range of 0.953–0.996 and mean relative errors in the range of 0.2–6.0%. Furthermore, the ANN yielded acceptable predictions for the system performance outside the range of the experiments. The results suggest that the ANN approach can alternatively and reliably be used for modelling cascade refrigeration systems. Copyright © 2006 John Wiley & Sons, Ltd.     

Prediction of selected biodiesel fuel properties using artificial neural network

Biodiesel is an alternative fuel to replace fossil-based diesel fuel. It has fuel properties similar to diesel which are generally determined experimentally. The experimental determination of various properties of biodiesel is costly, time consuming and a tedious process. To solve these problems, artificial neural network (ANN) has been considered as a vital tool for estimating the fuel properties of biodiesel, especially from its fatty acid (FA) composition. In this study, four ANNs have been designed and trained to predict the cetane number (CN), flash point (FP), kinematic viscosity (KV) and density of biodiesel using ANN with logsig and purelin transfer functions in the hidden layer of all the networks. The five most prevalent FAs from 55 feedstocks found in the literature utilized as the input parameters for the model are palmitic, stearic, oleic, linoleic and linolenic acids except for density network with a sixth parameter (temperature). Other FAs that are present in the biodiesels have been considered based on the number of carbon atom chains and the level of saturation. From this study, the prediction accuracy and the average absolute deviation of the networks are CN (96.69%; 1.637%), KV (95.80%; 1.638%), FP (99.07%; 0.997%) and density (99.40%; 0.101%). These values are reasonably better compared to previous studies on empirical correlations and ANN predictions of these fuel properties found in literature. Hence, the present study demonstrates the ability of ANN model to predict fuel properties of biodiesel with high accuracy.