Long Short term Power Load Forecasting Algorithm using Long Short Term Memory Neural Network with Density Based Spatial Clustering
In pursuance of refined accuracy of short term power load forecasting, this paper proposes Long Short Term Memory Neural Network with Density Based Spatial Clustering (LSTMNNDBSC) to forecast the short term power load. The proposed algorithm facilitates the extraction of original power data features using 8 dimensional load features; while the Density Based Spatial Clustering of Applications with Noise (DBSCAN) was employed to classify the extracted data and further determine the power load cluster. Furthermore, the Long Short Term Memory Neural Network (LSTMNN) was used to forecast the cluster of power load on the day of forecasting as well as to forecast the short term power load. It is evident from the experimental results that the algorithm based on 8 dimensional load curve features exhibited a better clustering effect and required lesser amount of processing time compared to the 96 dimensional load features of the original data. Similarly, the LSTMNNDBSC algorithm employed lesser training time and achieved higher forecasting accuracy compared to the comparative forecasting algorithm.
2019 IEEE 5th International Conference on Computer and Communications (ICCC)