Residential Load Forecasting Using Recurrent Neural Networks

In Electrical systems, load forecasting is very important as it has implications on flexibility, smooth operation, and economical aspects as well. The residential load depends on household size, weather season, numbers of load, number of occupants and their behavior, types of devices, etc. Thus, making its accurate forecasting a very difficult job. In this research, machine learning and deep learning-based Recurrent Neural Networks (RNN) algorithms are used for the day-ahead load forecasting of an Estonian household. A data set based on measured load values of an Estonian household is used in the development of this forecasting model. The simulation results indicate that the RNN based algorithm gives better forecasting based on lower Root Mean Square Error (RMSE) value.

[1]  Argo Rosin,et al.  Forecasting Short Term Wind Energy Generation using Machine Learning , 2019, 2019 IEEE 60th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON).

[2]  Lynne E. Parker,et al.  Energy and Buildings , 2012 .

[3]  Karl Aberer,et al.  Electricity load forecasting for residential customers: Exploiting aggregation and correlation between households , 2013, 2013 Sustainable Internet and ICT for Sustainability (SustainIT).

[4]  Borhan M. Sanandaji,et al.  Short-term residential electric load forecasting: A compressive spatio-temporal approach , 2016 .

[5]  Frédéric Magoulès,et al.  A review on the prediction of building energy consumption , 2012 .

[6]  Argo Rosin,et al.  Analysis of Household Electricity Consumption Patterns and Economy of Water Heating Shifting and Saving Bulbs , 2010 .

[7]  Seddik Bacha,et al.  Residential appliance identification and future usage prediction from smart meter , 2013, IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society.

[8]  Paul Williams,et al.  A status review of photovoltaic power conversion equipment reliability, safety, and quality assurance protocols , 2018 .

[9]  Lucio Soibelman,et al.  Learning Systems for Electric Consumption of Buildings , 2009 .

[10]  Omar Abou Khaled,et al.  Machine learning approaches for electric appliance classification , 2012, 2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA).

[11]  V. M. F. Mendes,et al.  Electricity demand profile prediction based on household characteristics , 2015, 2015 12th International Conference on the European Energy Market (EEM).

[12]  Argo Rosin,et al.  Comparison of Machine Learning Based Methods for Residential Load Forecasting , 2019, 2019 Electric Power Quality and Supply Reliability Conference (PQ) & 2019 Symposium on Electrical Engineering and Mechatronics (SEEM).

[13]  Claire J. Tomlin,et al.  Residential demand response targeting using machine learning with observational data , 2016, 2016 IEEE 55th Conference on Decision and Control (CDC).

[14]  Stefano Manetti,et al.  Measurement of Electric Power Quantities and Efficiency in Nonsinusoidal Conditions , 2018, 2018 AEIT International Annual Conference.