Short-term load forecasting by artificial neural networks specified by genetic algorithms – a simulation study over a Brazilian dataset

This paper studies the application of genetic algorithms in helping to select the proper architecture and training parameters, by means of evolutionary simulations done on a series of real load data, for a neural network to be used in electric load forecasting. Particularly, we investigate the application of a novel fitness function to the genetic algorithms, instead of the usual ones, based on the sum of the squares of the errors. We compare the results of the neural networks thus specified with that of four benchmarks: two naive forecasters, a linear method, and a neural network in which the parameter values are found by means of a grid search.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Short-term load forecasting by artificial neural networks specified by genetic algorithms – a simulation study over a Brazilian dataset

This paper studies the application of genetic algorithms in helping to select the proper architecture and training parameters, by means of evolutionary simulations done on a series of real load data, for a neural network to be used in electric load forecasting. Particularly, we investigate the application of a novel fitness function to the genetic algorithms, instead of the usual ones, based on the sum of the squares of the errors. We compare the results of the neural networks thus specified with that of four benchmarks: two naive forecasters, a linear method, and a neural network in which the parameter values are found by means of a grid search.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Multi-objective particle swarm optimization algorithm for multi-step electric load forecasting

As energy saving becomes more and more popular, electric load forecasting has played a more and more crucial role in power management systems in the last few years. Because of the real-time characteristic of electricity and the uncertainty change of an electric load, realizing the accuracy and stability of electric load forecasting is a challenging task. Many predecessors have obtained the expected forecasting results by various methods. Considering the stability of time series prediction, a novel combined electric load forecasting, which based on extreme learning machine (ELM), recurrent neural network (RNN), and support vector machines (SVMs), was proposed. The combined model first uses three neural networks to forecast the electric load data separately considering that the single model has inevitable disadvantages, the combined model applies the multi-objective particle swarm optimization algorithm (MOPSO) to optimize the parameters. In order to verify the capacity of the proposed combined model, 1-step, 2-step, and 3-step are used to forecast the electric load data of three Australian states, including New South Wales, Queensland, and Victoria. The experimental results intuitively indicate that for these three datasets, the combined model outperforms all three individual models used for comparison, which demonstrates its superior capability in terms of accuracy and stability

Local Short Term Electricity Load Forecasting: Automatic Approaches

Short-Term Load Forecasting (STLF) is a fundamental component in the efficient management of power systems, which has been studied intensively over the past 50 years. The emerging development of smart grid technologies is posing new challenges as well as opportunities to STLF. Load data, collected at higher geographical granularity and frequency through thousands of smart meters, allows us to build a more accurate local load forecasting model, which is essential for local optimization of power load through demand side management. With this paper, we show how several existing approaches for STLF are not applicable on local load forecasting, either because of long training time, unstable optimization process, or sensitivity to hyper-parameters. Accordingly, we select five models suitable for local STFL, which can be trained on different time-series with limited intervention from the user. The experiment, which consists of 40 time-series collected at different locations and aggregation levels, revealed that yearly pattern and temperature information are only useful for high aggregation level STLF. On local STLF task, the modified version of double seasonal Holt-Winter proposed in this paper performs relatively well with only 3 months of training data, compared to more complex methods