Over 33% of final energy consumption is used in buildings which leads to nearly 40% of total direct and indirect CO2 emissions in the world. While energy consumption is steadily rising globally, managing building energy utilization by on-site renewable energy generation can help responding this demand. This paper proposes a deep learning method based on a discrete wavelet transformation and long short-term memory method (DWT-LSTM) and a scheduling framework for the integrated modelling and management of energy demand and supply for buildings. This method considers several factors including a two-step electricity price, uncertainty in climatic factors, availability of renewable energy resources (wind and solar), energy consumption patterns in buildings, and the non-linear relationships between these parameters. This novel method analyzes and continuously learns from data patterns based on hourly, daily, weekly and monthly intervals. The method enables monitoring and controlling renewable energy generation, the share of energy imports from the grid, employment of saving strategy based on the user priority list, and energy storage management. The main objective of the proposed method is to minimize the reliance on the grid and electricity cost, especially during the peak days. The results demonstrate that the proposed method can forecast building energy demand and energy supply with a high level of accuracy, showing a 3.63- 8.57% error range in hourly data prediction for one month ahead. The results also show that the method can supply 304 days (83.2%) of a year without reliance on energy grids, decreasing 87.2% in energy demand on one hand and exporting annually 7777 kWh to the grid on the other hand. In addition, the rescheduling framework decreased the imported electricity cost with the higher electricity tariff by 98 %. The combination of the deep learning forecasting, energy storage, and scheduling algorithm enables reducing annual energy import from the grid from 6709 to 858 kWh (84.3%).

Energy has a strategic role in the economic and social development of countries. In the last few decades, energy demand has been increasing exponentially across the world, and predicting energy demand has become one of the main concerns in many countries. The residential and commercial sectors constitute about 34.7% of global energy consumption. Anticipating energy demand in these sectors will help governments to supply energy sources and to develop their sustainable energy plans such as using renewable and non-renewable energy potentials for the development of a secure and environmentally friendly energy system. Modeling energy consumption in the residential and commercial sectors enables identification of the influential economic, social, and technological factors, resulting in a secure level of energy supply. In this paper, we forecast residential and commercial energy demands in Iran using three different machine learning methods, including multiple linear regression, logarithmic multiple linear regression methods, and nonlinear autoregressive with exogenous input artificial neural networks. These models are developed based on several factors, including the share of renewable energy sources in final energy consumption, gross domestic production, population, natural gas price, and the electricity price. According to the results of the three machine learning methods applied in our study, by 2040, Iranian residential and commercial energy consumption will be 76.97, 96.42 and 128.09 Mtoe, respectively. Results show that Iran must develop and implement new policies to increase the share of renewable energy supply in final energy consumption.