Power forecasting and monitoring using Time-Series Analysis, SARIMA, Random Forest and LSTM
This work discusses smart building applications involving the Internet of Things (IoT) which are focused on energy consumption monitoring and forecasting systems, as well as indoor air quality (IAQ) control. Low-cost hardware integrating sensors and open source platforms are implemented for cloud data transmission, data storage and data processing. Advanced data analytics is performed by the seasonal autoregressive integrated moving average (SARIMA) method and a long short-term memory (LSTM) neural network with an accurate calculation performance about energy predictions.
The data acquistion hardware (IoT) and machine learning (python) was performed by me and the codes are uploaded here.
The weblink to the published work is this. https://www.mdpi.com/1969196
The results are communicated by me in the form of research paper attached here with title Advanced Data Systems for power predictive analytics_A.Tiwari.
@ Ing. Amber Tiwari (PhD)