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Applying Machine Learning Techniques to Short Term Load Forecasting

Lier, C (2015) Applying Machine Learning Techniques to Short Term Load Forecasting. Master's Thesis / Essay, Artificial Intelligence.

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This thesis reports on the application of two machine learning techniques on the case of 24-ahead short term load forecasting (STLF). The methods used are Random Forests and Echo State Networks. Hierarchical linear models are used as baseline comparison. Four different cases of STLF will be combined in this research: Total power consumption of an area, power demand on the power supplier, power supply to the power network, and solar power generation (SPG). These variables are useful things to know in power supply planning by power suppliers and short term peak detection for network operators. To know these variables beforehand means to be able to economically and securely operate the power grid and power supply. Therefore constant research is being done to improve forecasting techniques. More recently it has become important to incorporate the supply by users into the forecasting system as more and more households install solar panels. A dataset was used from a neighbourhood in The Netherlands where most households are outfitted with solar panels and all households have smart meters. A large part of the project consisted of cleaning the data. Predictors were chosen from the dataset using domain knowledge and partly by Fourier analysis. Some measurements of weather data were added to the dataset using an interpolation between two stations of the KNMI. Four datasets were created; one for each case. These were split up for training, validation, and testing purposes. Random Forests and Echo State Networks use a number of hyper-parameters as initiation or training settings. These parameters were optimized on the training and validation sets using particle swarm optimization (PSO). The resulting optimal settings were used to train new models and test performance on the test sets. Comparison was done by testing the differences in RMSE with Welch’s t-test. The results are interesting. It was found that the linear model is quite a good performer in most cases, but is sometimes outperformed by the Random Forest. Solar power generation has appeared to be the hardest to predict and even the linear model is not performing well in this case. The Echo State Network seems to be unsuitable for this kind of forecasting in all cases.

Item Type: Thesis (Master's Thesis / Essay)
Degree programme: Artificial Intelligence
Thesis type: Master's Thesis / Essay
Language: English
Date Deposited: 15 Feb 2018 08:10
Last Modified: 15 Feb 2018 08:10

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