Data-driven modeling using deep neural networks for power systems demand and locational marginal price forecasting

Jimu, Honest
Forecasting electricity demand and locational marginal prices (LMPs) have become critical components for power system security and management. Electricity Demand Forecasting (EDF) aids the utility in maximizing the use of power-generation plants and scheduling them for both reliability and cost-effectiveness. In this thesis, a novel Deep Neural Network Long Short-Term Memory (DNN-LSTM) forecasting model is suggested to improve accuracy and robustness for predicting hourly day ahead power system load and LMPs in two distinct markets, North Pool (NP), and New England-ISO (NE-ISO). Historical load, weather, statistical features derived from historical data, and system outage information (known as Line Outage Distribution Factors (LODFs)) will be used as input features in the proposed model. Two distinct demand-forecasting models will be modeled using two case studies that present different market patterns from different geographical locations. The deep neural network model will be compared with the state-of-the-art Lasso Estimated Autoregressive (LEAR) model using a variety of performance metrics, including Symmetric Mean Average Percentage Error (sMAPE), Root Mean Square Error (RMSE), Mean Average Percentage Error (MAPE), Relative Mean Average Error (rMAE), Mean Average Error (MAE). The results acquired from the two experimental case studies on the markets revealed that the proposed DNN model showed significant improvement in hourly demand and LMP forecasts and therefore outperformed contemporary statistical forecasting techniques in accuracy, computational time, and reliability.


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Citation Formats
H. Jimu, “Data-driven modeling using deep neural networks for power systems demand and locational marginal price forecasting,” M.S. - Master of Science, Middle East Technical University, 2022.