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Research Proposal

On

Medium Term Load Forecasting Using Artificial Neural

Networks

By

Sani Dahiru Buba

Department of Electrical & Electronic Engineering

Federal Polytechnic P.M.B 35 Mubi

650001, Adamawa State, Nigeria.

Email: sanibintahir@yahoo.com

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Introduction

Electricity is considered as a vital energy in the lives of human beings as a result of its numerous

applications which includes comfort heating and cooling, electric cookers, microwave cookers,

elevators, escalators in the airports and other public places, electric train, lighting etc. Economic

and industrial development is on the increase by the day, so also the standard of living.

Therefore, there is constant increase in demand of electricity from industries, residential and

commercial sectors, business as well as personal use. For this reason, electric utility

companies must constantly focus on the development of electric energy to meet the ever

increasing demand. This can be achieved through proper planning of expansion and monitoring

of load growth.

Forecasting is a phenomenon of knowing what may happen to a system when certain

trends or conditions continue or continue to change. In electrical power systems, there is a great

need for accurately forecasting the load and energy requirements because electricity generation

as well as distribution poses a great financial liability to utility companies. Accurate load forecast

provides system dispatchers with timely information to operate the system economically and

reliably. It is also necessary because availability of electricity is one of the most important

factors for industrial development, especially for developing countries [1].

Load forecasting is considered to be an important task in modern power system planning,

operation and control. Accurate load forecasting can improve the security of the power system

and promote the economic efficiency of electric utilities [2]. Furthermore, load forecasting is an

important component for energy management system. Precise load forecasting helps the electric

utility to make unit commitment decisions including decisions on purchasing and generating

electric power, load switching, and infrastructure development. Besides playing a key role in

reducing the generation cost, it is also essential to the reliability of power systems. Load

forecasting also helps to estimate load flows and to make decisions that can prevent overloading.

Timely implementations of such decisions lead to the improvement of network reliability and to

the reduced occurrences of equipment failures and blackouts. Load forecasting is also important

for contract evaluations and evaluations of various sophisticated financial products on energy

pricing offered by the market. In a deregulated economy, decisions on capital expenditures based

on long-term forecasting are more important than in a non-deregulated economy where a rate

increase could be justified by capital expenditure projects [1].

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Generally, load forecasting is categorized into three classes, short term, medium term and

long term. Smith [3] opined that, short-term load forecasts lasting for (five minutes to one week

ahead) are required to ensure system stability, medium term forecasts with a duration of (one

week to six months ahead) are required for maintenance scheduling, while long term forecasts

(six months to 10 years ahead) are required for capital planning. Hayati and Shirvany also

suggested that short-term load forecasting (STLF) refers to forecasts of electricity demand (or

load), on an hourly basis, from one to several days ahead. The short-term load forecasting (one to

twenty four hours) is of importance in the daily operations of a power utility. It is required for

unit commitment, energy transfer scheduling and load dispatch. With the emergence of load

management strategies, the short term load forecasting has played a greater role in utility

operations. Load forecasting is however a difficult task. First, because the load series is complex

and exhibits several levels of variability according to seasons. For instance, the load at a given

hour is dependent not only on the load at the previous hour, but also on the load at the same hour

on the previous day, and on the load at the same hour on the day with the same denomination in

the previous week. Secondly, there are many important exogenous variables that must be

considered, especially weather-related variables. It is relatively easy to get forecast with about 10

% mean absolute error; however, the cost of error are so high that any research that could help to

reduce it to a few percent points would be amply justified [4].

Furthermore, actual load of the power system at any point in time depends on a number

of factors, all of which cannot be accurately predicted. The load demand changes cyclically in

response to the seasonal variations. In general, the overall load demands of the system

continually increases due to industrial growth, increase in electricity consumption, and other

related factors. The load follows a definite pattern for each day of the week, but changes

considerably over the weekends and public holidays [5]. From the foregoing discussions

therefore, the significance of load forecasting in electrical power system cannot be over

emphasized and thus there is strong justification for conducting load forecast in power systems.

Literature Review

Many load forecasting models and methods have already been tried out with varying degrees of

success. They may be classified as time series models, in which the load is modeled as a function

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of its past observed values, and causal models in which the load is modeled as a function of some

exogenous factors, especially weather and social variables. Some frequently used models found

in literature are multiplicative autoregressive models, dynamic linear or nonlinear models,

threshold autoregressive models, and methods based on Kalman filtering. Despite this large

number of alternatives, however, the most popular causal models are still the linear regression

ones and the models that decompose the load, usually into basic and weather dependent

components. Many algorithms have also been proposed in the last few decades for performing

accurate load forecasting. The most commonly used techniques include statistically based

techniques like time series, regression techniques and box Jenkis models and computational

intelligence method like fuzzy systems, artificial neural networks (ANNs) and neuro-fuzzy

systems [4].

Dragomir et al. [6] proposed the use of Adaptive Neuro-Fuzzy inference system (ANFIS)

to study medium term load forecasting using data obtained from experiment on photo-voltaic

amphitheatre having parameters 0.4kV/10kW which is located in the east-central region of

Romania in the city of Targoviste. Artificial neural networks (ANN) is the most favourite

artificial intelligence (AI) tool in load forecasting especially for short term forecasting horizon

where it exhibits a good flexibility in capturing nonlinear interdependencies between the load

and exogenous variables. However, ANN models are complex and difficult to understand and are

often over fitted. This limits its application for all categories of load forecasting. ANFIS used in

non-periodic short term forecasting prediction introduces large errors due to high residual

variance, consequently, degrading prediction accuracy.

Hasan et al. [7] proposed a hybrid approach of ANN and particle swarm optimization

(PSO) model to study short term load forecasting (STLF) which is typical for western area of

Saudi Arabia using one year historical dependent data. A significant improvement was found

after applying PSO and ANN, it was also found that the error was within acceptable range for the

proposed method and the performance was much better than ANN alone.

Smith [3] studied electricity load and price forecasting using statistical methods and

models. The statistical method and model is an equivalent of ANN solution which is based on

the estimate of an additive semi parametric regression model resulting in the estimation of

smooth nonlinear periodic daily and weekly effects. If properly implemented, the forecasts are as

accurate as those from comparable ANN methods. Also, there are two major additional

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advantages of using statistical models, first, full predictive distribution of load are available, and

secondly, time series models for the residuals can be estimated which is a distinct advantage

because empirical evidence suggests that there is more structure to the data than nonlinear

periodic effects. Smith further reported a popular approach to load forecasting using multi-

equation models proposed by econometrics working on energy problems. Although they do not

estimate a smooth daily or weekly periodic function as in the case of semi parametric regression

model, it write down each intra-day period (such as half hour) as separate parametric regression

but with cross correlated errors. Multi-equation models have been widely used and are straight

forward to estimate either using maximum likelihood or Bayesian methods although there

appears to be no published comparisons, but the quality of the forecasts appear to be high. It is a

simple method to implement and has several advantages. First, it is known that meteorological

variables actually affect load in a different way in each of the intra-day periods which cannot be

captured by an additive meteorological effect used in the additive semi-parametric regression

models. Second, because the model is a series of parametric regressions, quite complex nonlinear

relationships between meteorological and load variables can be incorporated. Third, time series

models for the errors such as such as vector auto-regressions can be used. In this work, ANNs

would be used because of its high performance for prediction and also less requirement in terms

of memory space compared to other AI algorithms.

Objectives

The main objective of this study is to provide a medium term load forecast using ANN for a

chosen case study network. This is achieved by;

1. Obtaining previous load data from a chosen case study domain.

2. Develop an appropriate ANN structure and implement using the collected data.

Methodology

A case study electrical distribution network would be selected and the load profile studied either

by collecting the data over a period of time, say 6 months or load data from an electric utility’s

load profile over the same period of time would be used. The ANN structure would be developed

using the MATLAB ANN toolbox suitable to the collected data for training and testing purposes

after which a forecast routine would be implemented.

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Bibliography

[1] Sarangi, P. K., Singh, N., Swain, D., Chauhan, R. K., and Singh, R., Short Term Load

Forecasting Using Neuro-Genetic Hybrid Approach: Results Analysis with Different

Network Architectures, Journal of Theoretical and Applied Information Technology, 2009.

[2] Ko, C. N., Lee, C. M., Short Term Load Forecasting using Support Vector Regression based

Radial Basis Function Neural Network with Dual Extended Kalman Filter, Journal of Energy

Xxx (2013) 1-10.

[3] Smith, M., Electricity Load and Price Forecasting Using Statistical Methods and Models,

http://www.secondmoment.org/articles/electricity.php

[4] Hayati, M., and Shirvany, Y., Artificial Neural Network Approach for Short Term Load

Forecasting for Illam Region, International Journal of Electrical, Computer and System

Engineering, Vol. 1, No. 2, 2007.

[5] Srinivasan, D., and LEE, M. A., Survey of Hybrid Fuzzy Neural Approaches to Electric Load

Forecasting, International Conference on Man, Systems and Cybernetics, 22nd to 25th October,

Vancouver DC, Canada, 1995.

[6] Dragomir, O. E., Dragomir, F., Gouriveau, F., and Minca, E., Medium Term Load

Forecasting Using ANFIS Predictor, Presented at the 18th

Mediterranean Conference on

Control and Automation Congress, Marrakech, Morocco, 23rd

to 25th

June, 2010.

[7] Hasan, M. K., Khan, M. A., Ahmed, S., and Saber, A. Y., An Efficient Hybrid Model to Load

Forecasting, International Journal of Computer Science and Network Security, Vol. 10, No.8,

August 2010.

[8] Nagi, J., Yap, K. S., Nagi, F., Tiong, S. K., and Ahmed, S. K., A Computational Intelligence

Scheme for Prediction of the Daily Peak Load,

[9] De Felice, M., Short Term Load Forecasting with neural Network Ensembles: A Comparative

Study, IEEE Computational Intelligence Magazine, August 2011.