Forecasting monthly electric load and energy for a fast growing utility using an artificial neural network

Islam, Syed; Al-Alawi, S.M.; Ellithy, K.

doi:10.1016/0378-7796(95)00950-m

Cited by 72 publications

(27 citation statements)

References 8 publications

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“…An exhaustive description of neural networks applied to load forecasting containing the most important references to work in this area can be found in Metaxiotis et al (2003). Other papers of great interest concerning neural networks applications containing a variety of network architecture or type of data used (Sforna and Proverbio, 1995;Mohamed et al, 1998;Kiartzis et al, 1995;Islam et al, 1995).…”

Section: Energy Load Forecasting Using Neural Networkmentioning

confidence: 99%

Electric load analysis using an artificial neural network

Cavallaro¹

2005

Int. J. Energy Res.

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SUMMARYLoad forecasting in the current, increasingly liberalized, electricity power market is of crucial importance as a means for producers to optimize and rationalize energy supply. A number of electric power companies are equipped to make forecasts with the aid of traditional statistical methods. This paper presents the use of an artificial neural net to an hourly based load forecasting application for a small electric grid on an Italian island (Lipari) not connected to the mainland. The aim was to examine the forecasting ability of a neural net in a situation where the electric load was subject to considerable seasonal variations over the year. The variations are affected by energy demand related to the tourism season as well as by climatic conditions, especially temperature. The network developed was a multi-layer perceptron type built on three layers trained with a back-propagation algorithm. The input layer receives all the most relevant information regarding: the class of day type, the hour in the daytime, the load and background temperature recorded at the indicated time for the months of March, August and October whilst the output layer provides the forecast value at the indicated time in December. The results obtained are encouraging; in the training phase the RMS error rate was around 2% and this rate settled at about 2.6% during testing. As both the margins of error recorded are acceptable, the use of a neural network for electric load forecasting applications can be considered an attractive option.

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Section: Energy Load Forecasting Using Neural Networkmentioning

confidence: 99%

Electric load analysis using an artificial neural network

Cavallaro¹

2005

Int. J. Energy Res.

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“…Several research studies have been conducted over the last decades to explore this comp lex problem of monthly electricity demand forecasting by means of mult ivariate t ime series analysis [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32]7,[33][34][35]. Several studies have assumed that the input and output series are stationary and applied statistical models [16,36,17,[21][22][23]25,30,7].…”

Section: Introductionmentioning

confidence: 99%

Forecasting Short-term Electricity Demand in Residential Sector Based on Support Vector Regression and Fuzzy-rough Feature Selection with Particle Swarm Optimization

Son

Kim

2015

Procedia Engineering

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The aim of this study is to provide a precise model for one-month-ahead forecast of electricity demand in residential sector. In this study, a total of 20 influential variables are taken into account including monthly electricity consumption, 14 weather variables, and 5 social variables. Based on support vector regression and fuzzy-rough feature selection with particle swarm optimization algorithms, the proposed method established a model with variables that relate to the forecast without ignoring some of these variables one may inevitably lead to forecasting errors. The proposed forecasting model was validated using historical data from South Korea. Its time period was from January 1991 to December 2012. The first 240 months were used for training and the remaining 24 for testing. The performance was evaluated using M APE, M AE, RM SE, M BE, and UPA values. Furthermore, it was compared with that obtained from the artificial neural network, auto-regressive integrated moving average, multiple linear regression models, and the methods proposed in the previous studies, and found superior for every performance measure considered in this study. The proposed method has an advantage over the previous methods because it automatically determines appropriate and necessary variables for a reliable forecast. It is expected that the proposed model can contribute to more accurate forecasting of short-term electricity demand in residential sector. The ability to accurately forecast short -term electricity demand can assist power system operators and market participants in ensuring sustainable electricity planning decisions and secure electricity supply to the consumers.

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“…Added advantage with accurate MTLF is that deregulated firms can utilise the required information to guide the improvement of their transmission grid as well as distribution system. Economic impact of MTLF has been assessed in regulated and deregulated market since last two decades [32]. When energy is traded, accurate MTLF for monthly or yearly time-frame can help in better negotiations or purchase of energy, development of medium-term generation, transmission and distribution contracts [63].…”

Section: Mid-term Load Forecasting Overviewmentioning

confidence: 99%

“…Refs. [31][32][33] used number of electrical connections at the end of each month to define load demand of a region. In other cases, the aim was to formulate linear and mixed integer programming models to minimize total production costs of power generation in a region while satisfying a set economic, physical and environmental constraints [34].…”

Section: End-use Approachmentioning

confidence: 99%

See 1 more Smart Citation

Forecasting the load of electrical power systems in mid‐ and long‐term horizons: a review

Khuntia

Rueda

Meijden

2016

IET Generation, Transmission & Distribution

128

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Load forecasting has always been an important part in the planning and operation of electric utilities, i.e., both transmission and distribution companies. With technological advancement, change in economic condition and many other factors (to be discussed in this work), load forecasting is becoming more important. The forecast affects as well as gets affected because of the load impacting factors and actions taken in different time horizons. However, due to its stochastic and uncertainty characteristics, it has been one challenging problem for electrical utilities to accurately forecast future load demand. This paper aims at reviewing the different load forecasting techniques developed for the mid-and long-term horizons of electrical power systems. Since there has never been an explicit literature study of the various forecasting techniques for mid-and long-term horizons, this paper reviews techniques for each of the forecasting horizons, citing various methodologies developed so far supported by published literature. The study is concluded with discussion on future research directions.

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Forecasting monthly electric load and energy for a fast growing utility using an artificial neural network

Cited by 72 publications

References 8 publications

Electric load analysis using an artificial neural network

Electric load analysis using an artificial neural network

Forecasting Short-term Electricity Demand in Residential Sector Based on Support Vector Regression and Fuzzy-rough Feature Selection with Particle Swarm Optimization

Forecasting the load of electrical power systems in mid‐ and long‐term horizons: a review

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