Electric load analysis using an artificial neural network

Abstract

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.

Measuring Country Sustainability Performance Using Ensembles of Neuro-Fuzzy Technique

“…The findings of this paper showed that the combination of fuzzy logic and neural networks was an appropriate approach for the assessment of water supply sustainability. Cavallaro [23] used the neural network for a load application prediction of a small electric grid. The outcomes of this paper demonstrated that the proposed model was effective and had better performance compared with the traditional statistical approaches.…”

Effects of various factors on electric load forecasting: An overview

“…For example an industrial consumer consume more power during morning hours where as residential consumer consume more 978-1-4799-6042-2/14/$31.00 ©2014 IEEE power in evening. The important factors that should be taken into consideration for of electricity load forecasting can be classified as follows [7] • …”

Short-term load forecasting based on support vector regression and load profiling

“…The traditional methods known as stochastic time series models (with the autoregressive integrated moving average [ARIMA] and the autoregressive moving average model with exogenous inputs [ARMAX] models being the most popular) are being compared and even replaced by artificial intelligence–based methods. In the last two decades, the artificial neural networks (ANNs) became by far the most common methodology adopted in electricity load forecasting, having already proven considerable benefits in modeling the load behavior based on historical patterns .…”