基于ANN-PSO混合模型的电力需求预测

A. Anand, L. Suganthi
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引用次数: 29

摘要

发展中经济体需要投资能源项目。由于电力工程的酝酿期较长,准确预测其能源需求至关重要。本文采用粒子群优化和遗传算法结合的人工神经网络对印度泰米尔纳德邦未来的能源需求进行了预测。与更传统的建模方法相比,混合人工神经网络模型具有提供更好预测的潜力。将混合ANN-PSO模型与ARIMA模型、混合ANN-GA模型、混合ANN-BP模型和线性模型的预测结果进行了比较。粒子群算法和遗传算法都以线性和二次形式发展,并将混合人工神经网络模型应用于五时间序列。在所有混合神经网络模型中,基于RMSE和MAPE的ANN- pso模型是所有时间序列的最佳拟合模型。
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Forecasting of Electricity Demand by Hybrid ANN-PSO Models
Developing economies need to invest in energy projects. Because the gestation period of the electric projects is high, it is of paramount importance to accurately forecast the energy requirements. In the present paper, the future energy demand of the state of Tamil Nadu in India, is forecasted using an artificial neural network (ANN) optimized by particle swarm optimization (PSO) and by Genetic Algorithm (GA). Hybrid ANN Models have the potential to provide forecasts that perform well compared to the more traditional modelling approaches. The forecasted results obtained using the hybrid ANN-PSO models are compared with those of the ARIMA, hybrid ANN-GA, ANN-BP and linear models. Both PSO and GA have been developed in linear and quadratic forms and the hybrid ANN models have been applied to five-time series. Amongst all the hybrid ANN models, ANN-PSO models are the best fit models in all the time series based on RMSE and MAPE.
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