Modelling and Forecasting Electricity Consumption of the Philippines Essay

In the Philippines, electric automobile fountain is becoming the main berth draw relied upon in all stinting sphere of influences of the country. As condition goes by, while different establishments and properties were strengthened and true, the necessity for interior(prenominal)ated electrical thrust habit within the country accelerates. efficacy outlay is an grave index of the stinting ontogeny of a country. Rapid changes in sedulousness and the economy strongly affect talent custom. According to the Inter issue Energy yearly (IEA) in the year 2004, the Philippines had innate installed electrical thrust generating capacity of 15.1 gigawatts (GW). The country produced 53.1 billion kilowatt-hours (Bkwh) of electrical vital force in 2004, while consuming 49.4 Bkwh. accomplished thermal sources make up the heavy(p)st parcel of land of Philippine electrical energy fork up, comprising to a greater extent than 65 percent of the total in 2004.However, the Phi lippines is excessively the worlds certify-largest producer of ge othermic energy. Despite several electrical energy sources, in that respect be still bunch of problems that turn over much(prenominal) as electrical energy famine and high price just aboutwhat collectible to increase of drive and company expenses. The Philippines is non just the sole country that experiences these ad hoc dilemmas but the other countries in Asia wish rise Lebanon and Saudi Arabia, and the entire world too. This pushes re counters and experts to subject ara the pulmonary tuberculosis movement from the past old age in order for them to learn its manner and suggest a regularity to athletic supporter gradulate the actor companies and to prevent uncertainties that office happen in the near emerging. with the historic occlusive, at that place ar many another(prenominal) slipway and orders developed by the experts and one of them is border and prognostication. imitateling electric energy utilisation is personaful in planning and distribution by power utilities. mannequin is a process of generating abstract, conceptual,graphical and/or numeric puts. Models ar typically use when it is either unrealiz fitting or impractical to create info- radixd conditions in which scientists hindquarters directly posting outcomes. In the field of energy use direct to electricity, pretenceling is a genuinely strategic factor in heralding the co nameinous perform of electricity use. there atomic number 18 plenty of techniques and mathematical methods which argon already used and proven legal in determining the energy uptake much(prenominal) as Multivariate retroflexion digest, neural networks, autoregressive, and many more. Nowadays, while- serial analysis was tintly used in the electric energy role mock uping and soothsaying. In statistics, symptom processing, and mathematical finance, a time serial is a sequence of info points, measurable typically at successive time instants spaced at uniform time intervals. Based on Investopedia (2012) it provides another frameworking glide path which requires only data on the poseured multivariate, thence saving the user the trouble of determining influential variables and suggesting a form for the relation between them.For instance, amount the value of retail sales all(prenominal) month of the year would comprise a time series. This is because sales revenue is well defined, and consistently measured at evenly spaced intervals. Data collected crookedly or only once be not time series. Also, according to Austrilian billet of Statistics (2005) an observed time series pull up stakes the gate be decomposed into triple components the trend ( tenacious term direction), the seasonal (systematic, calendar related movements) and the irregular (unsystematic, inadequate term fluctuations). Models for time series data offer devote many forms and represent differ ent stochastic processes. just about other applications of time-series analysis argon in macroeconomics and finance. As of now, manakinling and portending is of its highest power point of achievement and signifi drive outce of the modern community especially in aiding certain dilemmas in electric function.Objectives1. To formulate a mathematical set for the electricity use of the Philippines. 2. To regard the Philippines monthly electricity use for the next three days. 3. To evaluate the formulated model in call the electricity utilisation. significance of the StudyThe goal of this choose is to be able to forecast the electrical energy exercise of the Philippines for the next three years finished formulating a model acquired from the data by victimization time-series. This information can contribute much to the power add on companies of the Philippines in order for them to determine the set of electricity consumption for the coming years. The forecasted precede wil l help them plan and prepare for whatever might happen in the upcoming years specifically in addressing the electricity shortage.Scope and LimitationThe carry on focuses primarily in modelling the electricity use of the Philippines by utilize the time-series analysis. The view is narrowed to the prevision of the monthly electricity consumption for the next three years of the entire Philippines. The data used in modelling is ground on the 1999-2011 record. revue of Related Literature manakin and omen electricity consumption of Malaysian large stigma mill about This study attempts to model and forecast the daily hurrying limit invite of Malaysian large sword mills and the yearbook maximum demand contributed by these trade name mills. It attempts to combine both(prenominal) the top-down and bottom-up snugglees to forecast the daily and one-year maximum demand of the stigma mills. The top-down improvement uses reasoning backward analysis to forecast the yearbook a mount of electricity consumption of the steel mills. The bottom-up approach uses the Model for Analysis of galvanic Demand Electric Load (MAED_EL) to convert the yearbook steel mills electricity consumption (which was previous obtained from the reverting model) into hourly vitiate of the steel mills. The proposed method shows good prediction verity, with hebdomadal Mean Absolute Percentage illusion (MAPE) of 2.3%.This study propose combination of the top-down and bottom-up methods to forecast the daily maximum demand of Malaysian large steel mills and the one-year maximum demand contributed by these steel mills. The top-down approach uses simple statistical regression analysis to forecast the one-year electricity consumption of these large steel mills, based on its kinship with annual steel take and vulgar domestic product. The projected annual electricity consumption from regression analysis was then integrated into the bottom-up model apply MAED_EL to construct t he hourly bear down curves. From the hourly demoralise curves, the daily and annual maximum demands of the steel mills are determined. This model has the ability to forecast accurately the daily maximum of the large steel mills, with MAPE of less than 3%.The proposed method however, is purely based on the assumption that the future trend of daily consumption follows the base year. Although this is a slight drawback, nevertheless the proposed method has provided the avail with a better performer to forecast steel mills bill, contempt the unavailability of daily takings data which is vital in forecasting. The outcome of this study will benefit the utility in ensuring rock-steady and economic operation of the theme grid, and is also useful for analysis pertaining to development of future optimal propagation and transmitting expansion plans. Findings of this study also give a valuable contribution to the utility in determining load heed strategies and designing of tariff str uctures.A possible approach to change the forecast cognitive process is by combining the model with a time series method such as ARIMA. This will enable the model to take into account the most new- do behaviour of steel mills load, and thus increase the accuracy of the forecast. The best approach however, would still be the one that is able to take into account the daily production data of the steel mills. With the availability of this instigateicular data, many other confused and more effective methods can be explored such as Artificial Neural internet (ANN) and fuzzy additive regression. These methods will study the ability to capture the factors that highly crook steel mills daily load such as daily production plan and maintenance schedule, and hence improve the accuracy of the forecast. (S. Aman et.al, 2011 )Long term energy consumption forecasting exploitation transmittable program Managing electrical energy provision is a complex task. The most important part of electric utility imagination planning is forecasting of the future load demand in the regional or national portion area. This is usually achieved by constructing models on relative information, such as climate and previous load demand data. In this news report, a heritable programme approach is proposed to forecast long term electrical power consumption in the area covered by a utility situated in the sou-east of Turkey. The empirical results demonstrate successful load forecast with a low error rate.In this paper, a genetic programing approach on the forecasting of long term electrical power consumption of a moderate city in Turkey was presented. It uses the genetic programing method to forecast future usage finished typic regression using annual data of the previous years.In stuffy regression, one has to decide on the appraisal function (can be an n-degree polynomial, non-polynomial, or a combination of both) and try to find the coefficients of this selected function. C onstructing an mind function can be a difficult task. in that respect is another form of regression called symbolic regression. In the symbolic regression problem, the aim is to search a symbolic representation of a model, instead of only searching for coefficients of a predefined model. Genetic computer programing (GP) method introduced by Koza can be used for the symbolic regression problem. GP searches for the model and coefficients of the model at the same time. In this study, power consumption data is processed with both conventional analysis and genetic programming techniques.Long term power consumption forecasting can provide important information for power distribution centers. precedent consumption in this city is promptly growing therefore accurate forecasts can help authorities to make reliable plans. In this work, a genetic programming based forecasting method is presented. twain other curve fitting methods are also presented for affinity with this technique. Data used in all three models are not preprocessed. Genetic programming technique is used to form a model and evaluate the parameters for the model. The goodness of the fit produced by the genetic programming method is evaluated using sum of squared errors (SSE) method, which is better than the other two methods of regression. It was proved that the genetic programming can be used for electric utility resource planning and forecasting of the future load demand in the regional or national service area effectively. (K. Karabulot et. al, 2008)electrical energy consumption forecasting in Italy using linear regression models The influence of economic and demographic variables on the annual electricity consumption in Italy has been investigated with the intention to develop a semipermanent consumption forecasting model. The time period considered for the historical data is from 1970 to 2007. Different regression models were developed, using historical electricity consumption, make domestic p roduct (gross domestic product), gross domestic product per capita (gross domestic product per capita) and population. A first part of the paper considers the estimation of GDP, price and GDP per capita elasticities of domestic and non-domestic electricity consumption. The domestic and non-domestic short run price elasticities are ensnare to be both approximately enough to 0.06, while long run elasticities are equal to 0.24 and 0.09, respectively. On the contrary, the elasticities of GDP and GDP per capita present high values. In the snatch part of the paper, different regression models, based on co-integrated or stationary data, are presented. Different statistical tests are engaged to check the validity of the proposed models.A comparison with national forecasts, based on complex econometric models, such as Markal-Time, was performed, display that the developed regressions are congruent with the positive projections, with deviations of 1% for the best case and 11% for the w orst. These deviations are to be considered acceptable in relation to the time span interpreted into account. This paper aims to estimate GDP, price and GDP per capita elasticities of domestic and non-domestic electricity consumption in Italy. Also this paper wants to forecast the future growth of these consumptions using different regression models and compare our results with other getable projections. The duck soup analysis showed that the price elasticity of domestic and non-domestic consumption is quite limited, confirming some results presented in previous studies. Through the findings, conclusions brace been acquired.First, there is no need to consider electricity price as explaining variable in forecasting models for Italian electricity consumption. Second, pricing policies cannot be used to kick upstairs the efficient use of electricity in Italy. The estimation of GDP and GDP per capita elasticities showed higher values with respect to price elasticities, demonstrating that the consumption response to GDP and GDP per capita changes is relevant. Therefore, there is the need to assure an appropriate aim of electricity supply to sustain the economic growth in Italy. According to the second target of the paper, different long-term forecasting models were developed and they substantially lead to similar results.Therefore, in the next years, an increase in the total electricity consumption, driven by both domestic and nondomestic consumptions, should be pass judgment in Italy with an average rate equal to about 2% per year. Assuming that the data reported represent the reference benchmark, it can guarantee the most accurate projections for total, domestic and non-domestic electricity consumptions respectively, because they fit the data. It is believed that the elasticities, forecasts and comments presented in this paper would be helpful to energy planners and insurance policy makers to build future scenarios about the Italian electricity consumption. (V. Bianco et. al., 2009)Forecasting electricity consumption in New Zealand using economic and demographic variables The inuence of selected economic and demographic variables on the annual electricity consumption in New Zealand has been investigated. The study uses gross domestic product, average price of electricity and population of New Zealand during the period 19651999. Models are developed using multiple linear regression analysis. It was found that the electricity consumption correlated effectively with all variables. Forecasts do using these models were compared with some available national forecasts. The forecasts are also compared with the forecasts of the previously developed Logistic model. Electricity consumption forecasting models based on economic factors for municipal and NonDomestic sectors and Total consumption for New Zealand using multiple linear regression have been proposed.The models performed effectively in the statistical tests conducted, implying their s ignicance in forecasting electricity consumption using the explaining variables considered. Comparisons of these models have been do with the national forecasts available in New Zealand. The comparison revealed that the forecasts made by the regression models are rattling comparable with the national forecasts. The accuracy of the forecasts made by these models depends strongly on the accuracy of forecasts made for the explaining variables. In this paper, simple regression had been used to model these variables. (Z. Mohamed & Pat Bodger, 2003)Modeling and Forecasting Electricity Demand in the Philippines The Philippine governing has deregulated electricity generation markets to encourage private investors and actively courted autarkic power producers (IPPs). This has been done to promote aptitude and reduce government financial debt obligations. Until the mid-1980s, the power sector in the Philippines was mostly state-owned through the National Power Corporation (NPC). aft(pre nominal) the debt crisis in the early 1980s, the Philippines government tightened pecuniary policy, and capital expenditure for additional electricity capacity was significantly reduced. This led to a slowdown in the electricity generating facilities. At the same time, electricity demand move to increase. This resulted in tight electricity supply and demand conditions by the middle of the 1980s. There continue to be periods of generating capacity constraints.This whitethorn be the result of difficulties in forecasting electricity consumption. The residential and industrial electricity demand in the Philippines is modeled. The analysis follows Johansens vector error correction approach to estimate the price and income elasticity in both long and short run. The results mention a long run cointegrating relationship is found among residential electricity consumption, income, and the carnation of electric appliances. In the industrial sector there appears to be a long-run relationsh ip holds for industrial electricity consumption and GDP. The lack of significant price responses appears to be the result of government development policies. The estimated models are used in forecasting total electricity consumption suggest that the governments official forecast for electricity demand would be on the upper bound of the forecast range. (K. Ishi & F. Joutz, 2009) Methodology1. pull data of the Philippines monthly electricity consumption from the year 1999 to 2011 in National Statistics Coordination notice to be used for constructing a time-series model for the electricity consumption. 2. Using the formulated model, forecast the Philippines electricity consumption for the next three years. 3. Through the acquired forecasted consumption, evaluate the performance of the model.ReferencesClough, L. (2008). Energy profile of Philippines. The encyclopedia of earth. Aman, S., Ping, H. , & Mubin, M (2011). Modelling and forecasting electricity consumption of Malaysian large steel mills. Scientific investigateand Essays Vol. 6 (8), pp. 1817-1830. ISSN 1992-2248. Karabulut, K., Alkan, A., & Yilmaz, A. (2008). Long term energy consumption forecasting using genetic programming. Mathematical and Computational Applications, Vol. 13, No. 2, pp. 71-80. Bianco, V., Manca, O., & Nardini, S. (2009). Electricity consumption forecasting in Italy using linear regression models. Elsevier Ltd. Energy 34 (2009) 1413-1421. Mohamed, Z. & Bodger, P. (2003). Forecasting electricity consumption in New Zealand using economic and demographic variables. Elsevier Ltd. Energy 50 (2004) 1833-1843.