Abstract
Many forecasting models are built on neural networks. The key issues in these models, which strongly translate into the accuracy of forecasts, are data representation and the decomposition of the forecasting problem. In this work, we consider both of these problems using short-term electricity load demand forecasting as an example. A load time series expresses both the trend and multiple seasonal cycles. To deal with multi-seasonality, we consider four methods of the problem decomposition. Depending on the decomposition degree, the problem is split into local subproblems which are modeled using neural networks. We move from the global model, which is competent for all forecasting tasks, through the local models competent for the subproblems, to the models built individually for each forecasting task. Additionally, we consider different ways of the input data encoding and analyze the impact of the data representation on the results. The forecasting models are examined on the real power system data from four European countries. Results indicate that the local approaches can significantly improve the accuracy of load forecasting, compared to the global approach. A greater degree of decomposition leads to the greater reduction in forecast errors.
Highlights
Short-term load forecasting (STLF) aims to predict the future load demand ranging from an hour to a week ahead
Since electricity load demand is the basic driver of electricity prices, load forecasting plays an important role in competitive energy markets
The final load forecast for each hour is found by a proper adaptive combination of the forecasts given for this hour by the three multilayer perceptron (MLP) modules
Summary
Short-term load forecasting (STLF) aims to predict the future load demand ranging from an hour to a week ahead. The final load forecast for each hour is found by a proper adaptive combination (based on the recursive least squares) of the forecasts given for this hour by the three MLP modules Another approach to STLF problem decomposition relies on clustering the NN inputs and designing a separate NN for each cluster. A load time series consisting of both global smooth trends and sharp local variations is decomposed into generalization and details, i.e., low- and high-frequency components These components are modeled separately by NNs. In [10], each NN gets as inputs a frequency component determined for the day d, next-day temperature and day-type index. Decomposition of the STLF problem at the distribution level into ‘‘regular’’ and ‘‘irregular’’ nodes based on load pattern similarities [30] The methods of representation of input and output data are shown below
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