Estimation of the Consumed Electric Energy by the Unschedulable Loads in a Nearly Zero Energy Building

This paper investigates the problem of the estimation of the energy consumed by the unschedulable electric appliances in a nearly-Zero Energy Building (nZEB) and specifically, the electric appliances that their operation cannot be programmed or scheduled by a home Energy Management System (EMS). This is important for the EMS because it is mainly based on the nZEB microgrid model and hence, the exact knowledge of the electric loads is required. In this paper, a novel method to predict the energy that will be consumed by the unschedulable loads is presented that is based on the Artificial Neural Network (ANN) technique. This is accomplished, by considering the weather forecast data and the residents’ habits of the house that will be estimated by considering the history of the electric energy consumption of the unschedulable appliances that can be obtained by the smart energy meters of the house. The effectiveness and the feasibility of the proposed energy consumption estimation algorithm are validated and evaluated by several simulation results in the MATLAB/Simulink environment, using real time electric energy measurements which were obtained by a typical house-hold, located in Northern Greece.

[1]  Stéphane Pouffary,et al.  Climate finance for cities and buildings : a handbook for local governments , 2014 .

[2]  Kaamran Raahemifar,et al.  A survey on Advanced Metering Infrastructure , 2014 .

[3]  Jovica V. Milanović,et al.  Forecasting Demand Flexibility of Aggregated Residential Load Using Smart Meter Data , 2018, IEEE Transactions on Power Systems.

[4]  Saifur Rahman,et al.  Load Profiles of Selected Major Household Appliances and Their Demand Response Opportunities , 2014, IEEE Transactions on Smart Grid.

[5]  Shirantha Welikala,et al.  Incorporating Appliance Usage Patterns for Non-Intrusive Load Monitoring and Load Forecasting , 2019, IEEE Transactions on Smart Grid.

[6]  Jie Gu,et al.  Short-term load forecasting using a long short-term memory network , 2017, 2017 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe).

[7]  Chongqing Kang,et al.  An Ensemble Forecasting Method for the Aggregated Load With Subprofiles , 2018, IEEE Transactions on Smart Grid.

[8]  Markos Koseoglou,et al.  Enhanced Effectiveness in the Appliance Scheduling and Energy Storage Control by utilizing the Particle Swarm Technique for a Nearly Zero Energy Building , 2018, 2018 IEEE 4th Southern Power Electronics Conference (SPEC).

[9]  Mohammad A. S. Masoum,et al.  Effects of V2H Integration on Optimal Sizing of Renewable Resources in Smart Home Based on Monte Carlo Simulations , 2018, IEEE Power and Energy Technology Systems Journal.

[10]  Arunesh Kumar Singh,et al.  Load forecasting techniques and methodologies: A review , 2012, 2012 2nd International Conference on Power, Control and Embedded Systems.

[11]  Yuan Zhang,et al.  Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network , 2019, IEEE Transactions on Smart Grid.

[12]  Mohamed Chaouch,et al.  Clustering-Based Improvement of Nonparametric Functional Time Series Forecasting: Application to Intra-Day Household-Level Load Curves , 2014, IEEE Transactions on Smart Grid.

[13]  Muhammad Khalid,et al.  An Improved Optimal Sizing Methodology for Future Autonomous Residential Smart Power Systems , 2018, IEEE Access.

[14]  Markos Koseoglou,et al.  A Novel Control Strategy for Improving the Performance of a Nearly Zero Energy Building , 2020, IEEE Transactions on Power Electronics.

[15]  Yuancheng Li,et al.  Short-Term Load Forecasting Based on the Analysis of User Electricity Behavior , 2016, Algorithms.

[16]  Pierluigi Mancarella,et al.  A short-term load forecasting model for demand response applications , 2014, 11th International Conference on the European Energy Market (EEM14).

[17]  David J. Hill,et al.  Short-Term Residential Load Forecasting Based on Resident Behaviour Learning , 2018, IEEE Transactions on Power Systems.

[18]  Kun Yu,et al.  Short-term Power Load Forecasting of Residential Community Based on GRU Neural Network , 2018, 2018 International Conference on Power System Technology (POWERCON).

[19]  Akhtar Kalam,et al.  Electricity load forecasting for Urban area using weather forecast information , 2016, 2016 IEEE International Conference on Power and Renewable Energy (ICPRE).

[20]  M. Etezadi-Amoli,et al.  Smart meter based short-term load forecasting for residential customers , 2011, 2011 North American Power Symposium.