Abstract
One of the major decision problems facing any electrical supply undertaking is the forecasting of peak power demand. A problem therefore arises when an estimate of future electricity demand is not known to prepare for impending possible increase in electricity demand. To overcome this problem, it is therefore imperative to evaluate the precise amount of energy required for a sustainable power supply to customers. In line with this goal, this study established a mathematical model of regression analysis using Pseudo-Inverse Matrix (PIM) method for the assessment of the historical data of Covenant University's electric energy consumption. This method predicts a more accurate and reliable future energy requirement for the community, with special consideration for the next one decade. The accuracy of prediction based on the use of PIM method is compared with the forecast result of the Least Squares Model (LSM), commonly used by engineers in making long-term forecast. The error analysis result from the Mean Absolute Percentage Error (MAPE) and the Root Mean Square Error (RMSE) performed on the two models using Mean Absolute Deviation (MAD) shows that the PIM is the most accurate of the models. Though this method is examined using a University community, it can be further extended to cover the whole country, provided the historical data of the country's past electric energy consumptions is available.Keywords: error analysis, historical data, linear regression, peak demand, pseudo-inverse matrix.JEL Classifications: C53, L94, Q47DOI: https://doi.org/10.32479/ijeep.7566
Highlights
The function of an electrical power system is to supply reliable and least cost electrical energy to electricity users
The mathematical model for the Annual Peak Electricity Demand for Covenant University as calculated for pseudo-inverse matrix (PIM) using the mathematical model of equation (10) is as presented in Table 2 and Figure 2 below
From the error analysis performed on the two methods, the PMI method has been proven to be mathematically superior and a more accurate method than the least squares model (LSM) which, like most time series model, proved to be inaccurate for a long-term forecast in view of saturation of the series
Summary
The function of an electrical power system is to supply reliable and least cost electrical energy to electricity users. Estimates of electricity requirements are crucial for appropriate planning of power system expansion, and this begins with a prediction of anticipated future load demands. It is very necessary because there needs to be an accurate picture of the future which many times is based on the past (Cullen, 1999) to prevent shortages in power supply to customers and even to prevent over-generation which will lead to wastage. Accurate models for electricity demand forecasting are crucial to the planning and operation of any organization involved in providing electricity for end-users as it helps the organization’s manangement to make valuable decisions on issues concerning power generation, load management and shedding, and development of the power system infrastructure (Feinberg and Genethliou, 2005). Excessive or inadequate contracting costs when buying or selling power in the balancing market can result in high financial losses which in extreme cases may lead to bankruptcy
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