Abstract
This work proposes two non-linear and one linear equation-based system for residential load forecasting considering heating degree days, cooling degree days, occupancy, and day type, which are applicable to any residential building with small sets of smart meter data. The coefficients of the proposed nonlinear and linear equations are tuned by particle swarm optimization (PSO) and the multiple linear regression method, respectively. For the purpose of comparison, a subtractive clustering based adaptive neuro fuzzy inference system (ANFIS), random forests, gradient boosting trees, and long-term short memory neural network, conventional and modified support vector regression methods were considered. Simulations have been performed in MATLAB environment, and all the methods were tested with randomly chosen 30 days data of a residential building in Memphis City for energy consumption prediction. The absolute average error, root mean square error, and mean average percentage errors are tabulated and considered as performance indices. The efficacy of the proposed systems for residential load forecasting over the other systems have been validated by both simulation results and performance indices, which indicate that the proposed equation-based systems have the lowest absolute average errors, root mean square errors, and mean average percentage errors compared to the other methods. In addition, the proposed systems can be easily practically implemented.
Highlights
The energy utilization in residential and commercial buildings all over the USA is almost 40%of the overall energy generation
The efficacy of the proposed systems for residential load forecasting over the other systems have been validated by both simulation results and performance indices, which indicate that the proposed equation-based systems have the lowest absolute average errors, root mean square errors, and mean average percentage errors compared to the other methods
Out of these data, consumption per day for comparison purposes and rest 304 days data were used for the adaptive neuro fuzzy inference system (ANFIS), randomly chosen 30 days (30 sets of data) data were used for the prediction of total energy consumption random forest, LSBoost, and long term short memory (LSTM) network methods for their training and validation
Summary
The energy utilization in residential and commercial buildings all over the USA is almost 40%of the overall energy generation. The energy utilization in residential and commercial buildings all over the USA is almost 40%. With the increase of luxury requirement of residents, the energy consumption is ever-increasing [1,2]. Providing the required power by grid is a hard task, especially during peak hours of the days. This problem can be solved in two ways. By implementing effective demand-side energy management system in the smart building that is capable of scheduling the load efficiently, the total cost of energy can be reduced by utilizing less loads that are operated by the grid power during the peak hours without affecting the consumers’ comfort demands [3,4].
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
