A novel Prediction Method based on Improved Deep Mixture Density Network for Electricity Consumption, Photovoltaic Generation, and Net Demand of Smart Homes: Case Study for Sydney Metropolitan Area

Bo  Yang; Zheng hua  Tao; Wei  Hong

doi:10.6180/jase.202407_27(7).0014

A novel Prediction Method based on Improved Deep Mixture Density Network for Electricity Consumption, Photovoltaic Generation, and Net Demand of Smart Homes: Case Study for Sydney Metropolitan Area

Environmental Engineering Computer Science and Information Engineering

Bo Yang¹This email address is being protected from spambots. You need JavaScript enabled to view it., Zheng hua Tao², and Wei Hong³

¹Changzhou University, Changzhou City, 213164, China

²NARI Technology Development Co., Ltd, Nanjing City 211106, China

³Changzhou University, Changzhou City, 213164, China

Received: June 14, 2023
Accepted: September 22, 2023
Publication Date: November 8, 2023

Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202407_27(7).0014

The electricity consumption in the smart grids consists of an uncertainty feature. Also, an unstable atmosphere situation causes photovoltaic (PV) generation will be undefined output. With both of these problems, the net demand power of consumers can’t be a specific value. On the other hand, using consumption patterns, the consumptions and generations could be predicted for improving the operation of the power system. This paper reports the results of the differential performance of probabilistic forecasting of the residential electricity consumption, PV power generation, and net demand related to smart buildings using the novel method of the Improved Deep Mixture Density Network (IDMDN). According to this, investigators used a strong Multi to Multi (M2M) mapping of the neural network model. They followed that they had used a kind of beta kernel to decrease the number of leakage issues. It is an attempt to generate random predictions by the method of end-to-end. It expressed a new performance of changed initiation and multiple procedures of educating to decrease or remove unsteady traits in the probabilistic problems of the beta kernel function. The results show good performance of the proposed method in comparison with other methods.

Keywords: Smart Buildings; Probabilistic forecasting; Photovoltaic; Net demand; Electricity consumption; Improved Deep Mixture Density Network

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