Deep Learning in Energy Modeling: Application in Smart Buildings With Distributed Energy Generation

Abstract

Over 33% of final energy consumption is used in buildings which leads to nearly 40% of total direct and indirect CO 2 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%).

Demand Response Program for Efficient Demand-Side Management in Smart Grid Considering Renewable Energy Sources

“…With rising population growth and advances in the industrial sector, it is estimated that residential and commercial energy demand will grow by 3% by the end of 2021 [2]. Buildings account for 33% of global energy consumption and 40% of CO 2 emissions, directly and indirectly, [3]. The global consumption of energy is increasing steadily.…”

Application of Deep Learning Model in Building Energy Consumption Prediction

“…In recent years, with the rapid development of human life, electricity consumption has also increased rapidly. Although solar energy, tidal energy, nuclear energy, wind energy, hydropower, and other clean and renewable energy sources have been widely used, today, fossil energy is still the main driving force for the development of human life as shown in Figure 1 [ 1 ]. The use of a large amount of fossil energy is undoubtedly accompanied by a large amount of greenhouse gas emissions.…”

Low‐carbon operation method of the building based on dynamic carbon emission factor of power system

“…Energy conservation and carbon reduction in building energy is an important way to achieve the global goal of 'carbon neutrality'. The final energy consumption of the construction sector accounts for about 33% of the total consumption, and the direct and indirect carbon dioxide emissions account for about 40% of the total global consumption [1]. From the energy supply and demand perspective, building energy consumption is on the demand side of the energy system.…”