FACULTY OF ELECTRICAL ENGINEERING. Eng. BINDIU Radu. PhD THESIS SUMMARY SHORT TERM LOAD FORECASTING

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1 Investeşte în oameni! FONDUL SOCIAL EUROPEAN Programul OperaŃional Sectorial Dezvoltarea Resurselor Umane Axa prioritară: 1 EducaŃia şi formarea profesională în sprijinul creşterii economice şi dezvoltării societăńii bazate pe cunoaştere Domeniul major de intervenńie: 1.5 Programe doctorale și postdoctorale în sprijinul cercetării Titlul proiectului: Proiect de dezvoltare a studiilor de doctorat în tehnologii avansate- PRODOC Numarul de identificare al contractului: POSDRU 6/1.5/S/5 Beneficiar: Universitatea Tehnică din Cluj-Napoca FACULTY OF ELECTRICAL ENGINEERING Eng. BINDIU Radu PhD THESIS SUMMARY SHORT TERM LOAD FORECASTING PhD Advisor, Prof.dr.ing.Mircea CHINDRIŞ Evaluation commission of PhD thesis: PRESIDENT: MEMBERS - Prof.PhD.Eng. Radu CIUPA - dean, Faculty of Electrical Engineering, Technical University of Cluj-Napoca; - Prof.PhD.Eng. Mircea CHINDRIŞ PhD Advisor, Technical University of Cluj-Napoca; - Prof.dr.ing. Nicolae Golovanov - referent, Politehnica University of Bucureşti; - Prof.dr.ing. Dorin Sarchiz referent, Petru Maior University of Târgu-Mureş; - Conf.fiz.dr.ing. Andrei Cziker - referent, Technical University of Cluj-Napoca.

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3 Energy sector liberalization led to a series of changes in production, supply, transmission and distribution of electricity in Romania. The appearance of different markets that make up the wholesale electricity market in Romania led the participants to adapt to the imposed conditions. The economic impact was strong both to producers and especially to the consumers. Along with the competitive market continued to operate the regulated market. The choice for a regulated tariff or the negotiation of one, allowed the consumers the selection of an incumbent supplier or a competitive supplier according to the lowest price offered. In this context, the markets with hourly settlement (DAM and BM) have imposed individual prices for the electricity consumption according to the time scales. Therefore, load curves require a necessary tool to measure this product. Load forecasting has a significant impact on the operation and functioning of national energy system. The biggest influence of forecast accuracy is considered to be on the economical level. Consumer involvement, with the supplier is required if the penalties due to imbalances created by the consumption forecast inaccuracy can lead to some costs that directly affect the activity of the two participants. The reference price is usually the spot market price (DAM) and therefore short-term load forecasting, specifically for next day, has a special importance in reducing the bill of this product. In this context, evaluating various opportunities to acquire electricity from different energy markets is a necessity, especially assessment of total costs, taking into account both scenarios including bilateral contracts to purchase in base, peak and off-peak energy and trading on DAM and BM. The financial aspect stated above is based on the results achieved by load forecasting, describing the appearance of commercial activity that implies a liberalized electricity market. Both situations are analyzed in the paper, just to emphasize the importance and impact of short term load forecasting. This paper presents an analysis of the wholesale electricity market in Romania, taking into account the evolution of the most important parameters that describe its operation. The year 2006 marks an unprecedented development due to the emergence CMBC, DAM and BM, determining a transparent trading energy market. The evolution of prices and quantities traded since 2006 was necessary to perform a complete analysis, which presents the main connections between existing markets and the main influences on the prices. Conclusions arising from this analysis revealed that PIP is directly influenced by the amount of energy traded on CMBC, the electricity consumption, the water flow on river Danube, the temperature and the cross-border trade balance caused by the ATC. Not only spot market prices influence the evolution of costs, but also specific prices obtained on the balance market. Thus, the amount of energy contracted on ASM directly affects both surplus and deficit prices and the amount of energy offered on the DAM. It is mandatory that the electricity contracted on ASM to be tendered on the BM. This action determines the spot market prices to increase or decrease in relation to the quantity of energy traded on ancillary system market. To the energy quantity offered for the three types of regulation on the BM is added the national consumption forecasting error registered by the OTS, both parameters having a decisive impact on price developments of surplus and deficit. In this situation, short-term load forecasting is necessary to reduce costs. This forecast should be related to different types of consumers taking into account the particular aspects that each of them present. The paper continues with an overview of various consumers made according to particular aspects of the short-term load forecasting. The factors influencing the evolution of electricity consumption represent the basis for load forecasting. This paper reviews a number of parameters that take into account the weather, or other specific factors. However, from other work experience, the temperature seems to be the most important factor affecting electricity consumption. The description of the main methods used in practice for short term load forecasting represents another part of this work. The statistical models that use time series and the other causal models that use, or not, artificial intelligence determine the satisfactory results. However, using ANN MLP models were imposed in the literature as the most adaptive and flexible to the load forecasting issue. In order to reduce the forecast errors, hybrid models using ANN MLP with ARIMA or double seasonal exponential smoothing, but especially neural networks with fuzzy inference systems, were utilized. These methods provide good results and allow the integration of specific variables based on the experience of specialists in the field. In the literature there are a number of other proposals that present results with different forecast accuracy. The current state research in the field of load forecasting highlights the need and utility of modern techniques. In fact, in our country, a series of works are

4 reported using, ANN MLP or recurrent ANN, but the results are worse than for papers abroad. As mentioned above, the short term load forecasting has important economical implications at both the supplier and the consumer. In case of a single consumer, the volatility consumption is determined by the specific characteristics and it is much higher than in the case of the supplier. Therefore, the absolute percentage errors are larger. This paper performs an analysis on a case study utilizing methods often used in practice for the load forecast of an industrial consumer. Thus, a determination is made using a multi-linear regression model. Empirically, it has been tested a number of parameters describing the weather, but the model that uses only the history of consumption has obtained the best results. The second method tested was double seasonal exponential smoothing. In order to determine the specific coefficients of this method a genetic algorithm was applied. The multitude of variants that should have been tested imposed their use. The mathematical algorithm utilized is an original one that uses real coding for the four double seasonal exponential smoothing specific coefficients, determining the optimal solution to the minimum square error of the recorded data set. The two variants described have not provided satisfactory results and therefore it has been used a third model that utilizes ANN MLP. Although empirical tests have been made on a number of input variables, integrating weather factors, the model with the lowest errors uses only the history of consumption of the previous day and of the similar day for the previous week. In order to obtain a small error is required an optimal configuration of network architecture. In order to determine the optimal topology, a genetic algorithm with Pareto optimization technique (NSGA-II) has been used. Thus, the optimal topology was determined taking into account the average absolute percentage error when running the algorithm for training set and the minimum amount of neurons in the first and second hidden layer. The original technique, NSGA-II, is implemented and validated mathematically in This method provides the best results of the three options analyzed. The proposed model was adjusted through a mechanism that uses a fuzzy inference system. Thus, the experience of the specialists was integrated in order to reduce forecast errors especially during the working day, because the consumption in this period is about 10 to 20 times higher than in the remaining time. The proposed model is original and has two input variables that characterize the technological processes and any specific events or contingencies, as well as the time period in which they occur. This method is used to adjust the load forecast made by the ANN MLP. The absolute percentage error is reduced with around 2% - 3% and the absolute percentage error with about 20% - 30%. Also, the distribution of absolute percentage error is improved with the usage of this model. A larger reduction of errors requires a much closer collaboration with the energy management department of the industrial consumer and a sincere cooperation, as well as increased interest for data collection and interpretation. If the absolute percentage errors for industrial consumers can easily exceed 10%, for large suppliers these numerical values are not considered acceptable. In scientific literature, competitive suppliers can use next day load forecasting errors within the range of 2% to 3%. The paper proposes a forecasting model using ANN MLP for an incumbent electricity supplier. Empirically, it have been tested a large number of models, but one that provided the best results use daily average, minimum and maximum temperature values, plus the nebulosity. So far in Romania it has not been managed successfully the integration of a variable that takes into account the cloud cover. After analyzing the results of the proposed forecast model, the integration of a neuron that takes into account the cloudiness of the day has improved the absolute percentage error by 1%. What should be mentioned is that a further integration of temperature reduces the absolute percentage error recorded. Mean absolute percentage error value for the period under review has an average below the value of 2.5%. In order to validate this model the forecast was extended for the next period. As the costs increase with the unbalances due to imprecision of the forecast, it was considered useful to adjust the existing method through a statistical technique applied to the absolute percentage error. For this purpose it has been used an ARIMA (2,0,2) 1 model for the predicted error, a model that is applied only if the time series is stationary. The mean absolute percentage errors is reduced with only 0.2% for the period under review, noting that in the last week using this proposed method, a reduction of MAPE to 1.9%, below 2%, is considered to be low in the scientific literature. The forecasting model used was extended to the holidays. Due to very poor results achieved from using this model for special days, a number of variables were empirically tested taking into consideration the history of consumption of previous years. The originality of the proposed model is that it integrates the history of consumption and the specific weather factors of the past years, as well as a variable that takes into account the economic 2

5 development of the country, namely GDPT. This parameter has not been used in short-term load forecasting. Error is reduced by about 1% in comparison with the model that uses only consumption history and the weather factors. Adjustments were made for different days of Easter and Christmas, but the conclusion and the results were equally good. If for religious holidays GDPT was used in order to forecast next day s load, for the 1 st December this parameter has a negative influence. This negative influence is caused by the fact that the 1 st December is considered more a day of rest than a holiday and the consumption is influenced by the history of the past years and recently recorded load values for the 30 th November. Mean percentage errors in each of the three cases are below 3% and are considered to have high accuracy. Another problem is the integration of the entertainment events, and especially sports into the next day load forecast. Interest in them has a great impact on the evolution of consumption. The analysis of a particular incumbent supplier shows that the consumption growth can be modified as more as 4% during a significant sport event. In this context, a model has been build in order to adjust the forecast of the suppliers load. This model is obtained by incrementing the forecasted load of the ANN MLP model with a specific value obtained due to the influence of these events. In order to adjust the ANN MLP model it has been used a fuzzy inference system that integrates five factors that describe the specific of the sporting events. For simplicity, the author has appealed to their merging into two input variables which have been used latter for fuzzy inference system. For this analysis were chosen 20 specific days in The multitude of sporting events has allowed the analysis of many types of sports since in this period took place the European Football Championships and Summer Olympics. Outcomes show a decrease in absolute percentage error of 0.5% daily and maximum percentage error reported during the time scale of the particular day when the events took place, of around 1%. It should be noted that this model reduces errors only for football and Olympics, sports like Formula 1 can not rely on this method. If load forecast accuracy describes the commercial risk, the financial risk is described by the possibilities of buying and selling electricity on the wholesale market. In Romania, energy acquisition can be done either through bilateral contracts or by trading on the DAM. The high risk of energy trading on the DAM impels the electricity suppliers to purchase this product through bilateral contracts. Thus, energy is purchased on base, peak and off-peak through bilateral contracts. The profile of consumption is obtained on the DAM and the registered unbalances determined by the forecast imprecision will be taxed on the BM. In this context, full cost estimation is required for the electricity sold or purchased. The paper proposes a forecasting model for monthahead deficit and surplus prices. The proposed model is an original one that uses some specific parameters related to the evolution of the electricity production and the history of prices. Errors recorded for the specific indicators fall within acceptable values in case of positive unbalances. The increased volatility of the negative unbalances prices does not allow this model to be used in practice. The most important fact related to the BM price forecasts remain the maximum values for the deficit rate and the minimum values for the surplus rate. These two projected values have a MAPE of about 5% for deficit price and 20 RON for the surplus rate, and therefore, the proposed model can be used successfully in practice. There is currently no equivalent comparison in the literature. Factors that are used to forecast prices in Romania on BM represent an element of originality, as well as the use of neural networks for BM price forecasting. Also, this paper proposes a forecasting model for one month ahead spot market prices. The model uses ANN and incorporates similar variables that were included in the BM prices forecasting. The errors obtained have recorded average values for the specific indicators below 10% (peak and off-peak). If off-peak period, the MAPE is higher due to price volatility and significantly lower values. In the two years analyzed, the results can be considered good for both the general and the particular week-days. Another aspect that influences the electricity costs is load forecast error. Because any agreement requires a load forecast and a range of fluctuation around it, the error impact on the electricity bill is essential. The "classic" method calculates costs depending on this imposed range for the load variation. From this point of view there are two scenarios: the first calculates all costs according to a positive variation of the load and the second one calculates all costs related to a negative load variation. This perspective does not allow a real cost evaluation because the load fluctuation during one day can be negative and positive. Therefore, it has been proposed a method to determine the total cost with a load variation up to 30%, according to the most unfavorable load profile. Their determination, checking all possible alternatives would have required quite a long 3

6 time. Therefore, the author proposed a model using genetic algorithms for determining the most unfavorable load profile for each month, taking into consideration the electricity consumption of a virtual client. Outcomes show that in this case the costs are higher than those recorded in use of "classic" model and their evaluation is much closer to reality. There are consumers with a chaotic load curve, due to the technological flow. Variations in this case can reach even 100% of actual consumption. It is therefore important to assess the influence of forecasting error variance in terms of total monthly consumption level. In this case, the percentage change is calculated as the total energy unbalances related to total expected consumption of that month. By this, the author proposes a new way of calculating the maximum cost in relation to the variation of 100% in the hours that take into account both load profile and prices recorded on the BM. Total costs take into account a virtual additional consumption during hours with the most disadvantageous BM prices. Of course, this case is the most general of all the three cases presented and will record the highest costs. In the liberalized Romanian electricity market, possibilities of purchasing electricity are different and require a thorough analysis in order to control the risk, taking into account the distribution of energy through bilateral contracts, DAM and reporting on all costs to BM. In this respect, the author proposes a method to assess the final cost using forecasting models and optimization of costs above. Thus, based on the load forecasts and integrating the virtual projected prices on DAM, it has been proposed a methodology for calculating the maximum amount of energy in base, peak and off-peak purchased through bilateral contracts. A genetic algorithm and Pareto optimization method have been used for their calculations. The objective function has two criteria that help to shape the NSGA-II method: the first is the maximum amount purchased through bilateral contracts, and the second calculates the minimum cost. To calculate the full costs including unbalances traded on BM, the prices used are the minimum surplus and the maximum deficit rates. The analysis includes the most disadvantageous profile recorded in the same month of the past year. It also may indicate that the "classic" method shows a greater positive difference by about 10 lei in contrast with the "the most disadvantageous profile" method. In other words, the estimation of the real price has much higher precision when using the method proposed by the author than with conventional method. Optimal tariff choice for a consumer is very important in reducing electricity costs. This paper presents a detailed analysis for choosing the optimal regulated tariff for a public lighting consumer. The result is somewhat predictable and confirms that the lowest price for a kwh consumed is registered for the night specific rate. Based on the methods proposed above a virtual instrument has been developed. This software allows the calculation of the optimal tariff, determine the optimal energy purchased on the DAM and through bilateral contracts and assessment costs depending on the most disadvantageous profile. This program is described in detail in the Annexes. As a result of issues prior presented, it can be highlighted that the importance of short term load forecasting has a particularly important impact on costs. Author's main contributions are: complete analysis of the wholesale electricity market in Romania, which includes a prices recorded analysis, quantities traded and the influence of market participants on these two indicators; types of consumer synthesis and analysis of the main factors affecting the evolution of short term electricity consumption ; systematic presentation of short-term load forecasting methods currently in use; load forecasting model using multi-linear regression for an industrial consumer; load forecasting model for an industrial consumer using double seasonal exponential smoothing. The specific coefficients of this method were obtained by using a genetic algorithms with real coding; load forecasting model for an industrial customer using ANN MLP. Optimal network topology was obtained using a genetic algorithm with NSGA-II optimization method, taking into consideration an objective function with two imposed criteria; the integration of the specialists experience through a fuzzy inference system; 4

7 the development of a virtual instrument that allows the load forecast using the following methods: multi-linear regression, exponential smoothing and ANN MLP with a series of five specific training algorithms; day-ahead load forecasting model for an incumbent supplier, which manages to successfully integrate variable that takes into account the cloud cover; load forecasting model using ARIMA in order to reduce errors; load forecasting model for special days (religious holidays of Easter and Christmas). The mode proposed reduces forecasting errors by integrating the GDPT factor; forecasting model for the 1st December (national holiday); development of a virtual instrument for integration of major sporting events in the short term load forecasting using a fuzzy inference system; forecasting model for the next month average BM prices using ANN MLP; general forecasting model for the next month electricity spot prices (base, peak and off-peak); the proposal of two new methods to assess the influence of the load forecast on the total electricity costs (the "most disadvantageous profile" and the "maximum cost" methods); determine the optimal energy quantity purchased through bilateral contracts in peak and off-peak using an NSGA-II Pareto optimization; the estimation of electricity total costs for a virtual consumer using the method "the most disadvantageous profile"; the development of virtual instrument that allows the optimal tariff selection, determining the optimal quantity purchased through bilateral contracts and maximum costs of electrical energy. After the aspects presented in the prior paragraphs, the following directions for further research are proposed by the author: the development of additional methods in order to reduce further more absolute percentage errors; extending and adapting the methods used for the next day load forecasting to a period of one week and one month; the integration of several parameters in the load forecast for a more accurate modeling; the development of models for long-term forecast prices and the reduce of errors through extending and modifications of the current models; the implementation of similar methods for electricity production forecasting; the development of methods that are able to integrate the total costs of producers taking into consideration the markets on which they participate; the development of a comprehensive algorithm involving energy trading in other European Markets in order to reduce costs and maximize profits. The results obtained during the research for the develop of the PhD thesis were presented at national and international conferences in over 20 articles as: R., Bindiu, A., Cziker, G.V., Pop, Optimum tariff selection for public lighting systems, in proceedings of The 5th International Conference ILUMINAT 2009, 20 February 2009, Cluj Napoca, Romania, pp , ISBN R., Bindiu, M., Chindriş, C.O. Gecan, Dedicated software for optimal choice of energy tariffs in Romania, in proceedings of Conference of Energy Engineering Clean and Available Energy CIE 2009, 4-5 June 2009, Oradea, Romania, pp , ISSN R., Bindiu, M., Chindriş, C.O., Gecan, Short Term Load Forecasting for the National Electricity Consumption in Romania, in proceedings of 7 th INTERNATIONAL CONFERENCE ON ELECTROMECHANICAL AND POWER SYSTEM, SIELMEN 2009, 8-9 Octombrie 2009, Iaşi, Romania, Volume 1, pp.25-29, ISSN R., Bindiu, M., Chindriş, G.V., Pop, Day-Ahead Load Forecasting Using Exponential Smoothing, in proceedings of The 4th edition of the Interdisciplinary in Engineering International Conference INTER- ENG 2009, November 2009, Târgu Mures, Romania, pp.10-14, ISSN X. 5

8 R., Bindiu, M., Chindriş, G.V., Pop, C.O., Gecan, Forecasting Spot Base Electricity Price in Romania using Artificial Neural Networks and ARIMA Time Series Model, in proceedings of The 3rd edition of the International Conference on Modern Power Systems MPS 2010, Acta Electrotehnica, May 2010, Cluj-Napoca, Romania, pp , ISSN R., Bindiu, M., Chindriş, C.O., Gecan, G.V., Pop, R., Vasiliu, D., Gheorghe, Short Term Load Forecasting a Distribution Operator in Romania, The 16 th Conference of Energy Engineering CIE 2010, May, Oradea, Baile Felix, Romania. R., Bindiu, G. V. Pop, C. O Gecan, Impactul integrării pieńei intra-day de energie electrică asupra costurilor de echilibrare în România, Volumul de Lucrari Simpozionul InternaŃional EficienŃa Energetica 2010,7-9 octombrie 2010 Cluj-Napoca, EdiŃia a VII-a, pp.27-37, ISBN R., Bindiu, M., Chindris, G.V., Pop, C.O., Gecan, Short Term Load Forecasting for an Industrial Consumer in Romania, in proceedings of The 4th edition of the International Conference on Modern Power Systems 2011, Acta Electrotehnica, May 2011, Cluj-Napoca, Romania, pp