Modelling and Forecasting Electricity Consumption of the Philippines’

Topics: Linear regression, Forecasting, Regression analysis Pages: 9 (2885 words) Published: February 25, 2013
Thesis Concept Paper
Proposed Title: ‘Modelling and Forecasting Electricity Consumption of the Philippines’ Researcher: Alejon P. Padriganda
Degree Program:Bachelor of Science in Applied Mathematics
Adviser:Dennis A. Tarepe Ph.D

Backgorund of the Study
In the Philippines, electric power is becoming the main energy form relied upon in all economic sectors of the country. As time goes by, while different establishments and properties were built and developed, the demand for domestic electricity consumption within the country accelerates. Energy consumption is an important index of the economic development of a country. Rapid changes in industry and the economy strongly affect energy consumption. According to the International Energy Annual (IEA) in the year 2004, the Philippines had total installed electricity generating capacity of 15.1 gigawatts (GW). The country produced 53.1 billion kilowatt-hours (Bkwh) of electricity in 2004, while consuming 49.4 Bkwh. Conventional thermal sources make up the largest share of Philippine electricity supply, comprising more than 65 percent of the total in 2004. However, the Philippines is also the world’s second-largest producer of geothermal energy. Despite several electricity sources, there are still bunch of problems that occur such as electricity shortage and high price somewhat due to increase of demand and company expenses. The Philippines is not just the sole country that experiences these specific dilemmas but the other countries in Asia like Lebanon and Saudi Arabia, and the entire world too. This pushes researchers and experts to study the consumption movement from the past years in order for them to learn its behavior and suggest a method to help prepare the power companies and to prevent uncertainties that might happen in the near future. Through the years, there are many ways and methods developed by the experts and one of them is modeling and forecasting.

Modelling electric energy consumption is useful in planning and distribution by power utilities. Modeling is a process of generating abstract, conceptual,graphical and/or mathematical models. Models are typically used when it is either impossible or impractical to create experimental conditions in which scientists can directly measure outcomes. In the field of energy use direct to electricity, modelling is a very important factor in forecasting the next set of electricity consumption. There are plenty of techniques and mathematical methods which are already used and proven effective in determining the energy consumption such as Multivariate regression –analysis, neural networks, autoregressive, and many more. Nowadays, time-series analysis was also used in the electric energy consumption modeling and forecasting. In statistics, signal processing, and mathematical finance, a time series is a sequence of data points, measured typically at successive time instants spaced at uniform time intervals. Based on Investopedia (2012) it provides another modeling approach which requires only data on the modeled variable, thus saving the user the trouble of determining influential variables and suggesting a form for the relation between them. For instance, measuring the value of retail sales each month of the year would comprise a time series. This is because sales revenue is well defined, and consistently measured at equally spaced intervals. Data collected irregularly or only once are not time series. Also, according to Austrilian Bureau of Statistics (2005) an observed time series can be decomposed into three components: the trend (long term direction), the seasonal (systematic, calendar related movements) and the irregular (unsystematic, short term fluctuations). Models for time series data can have many forms and represent different stochastic processes. Some other applications of time-series analysis are in macroeconomics and finance. As of now, modelling and forecasting is of its highest peak of...
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