Mid-Term Power Load Forecasting of a Statistically Modified Long-term Data by using the LSTM
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Keywords:
Load Forecasting, Long Short-Term Memory, Machine Learning, Short-Term Load Forecasting, Time-Series AnalysisAbstract
The surplus power produced by power plants, which is considered as generation losses, can be
avoided by estimating the expected load consumption, which will lead to financial gains for companies
producing electrical energy. An accurate estimation of the power load can yield a reliable determination
for power system management and the accompanying reduction of gas emitted from power plants. This
work aims to create an integrated deep learning model based on a time series index to estimate future
values of electric power consumption by applying Long Short-Term Memory (LSTM) networks. The
dataset used has taken directly from PJM Interconnection Organization, which is a regional transmission
organization in the United States, the data is an hourly power consumption in megawatt for Chicago and
much area of Northern Illinois state. A statistical test was used to evaluate the dataset. Three different
statistics functions have used for resampling the dataset, mean, minimum, and maximum function. After
fitting the proposed model, it will predict the power load for one year ahead on daily basis. When the
minimum function has used in the resampling processing the model was able to attain a Mean Absolute
Percentage Error (MAPE) of 3.84%, and the coefficient of determination (R-squared) of 0.8.
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