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

Buildings are responsible for 33% of final energy consumption, and 40% of direct and indirect CO2 emissions globally. While energy consumption is steadily rising globally, managing building energy utilization by on-site renewable energy generation can help responding to 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 analyzes several factors including electricity price, uncertainty in climatic factors, availability of renewable energy sources (wind and solar), energy consumption patterns in buildings, and the non-linear relationships between these parameters 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 to minimize the reliance on the grid and electricity cost, especially during the peak hours. 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 combination of the deep learning forecasting, energy storage, and scheduling algorithm enables reducing annual energy import from the grid by 84%, which offers electricity cost savings by 87%. Finally, two smart active buildings configurations are financially analyzed for the next thirty years. Based on the results, the proposed smart building with solar Photo-Voltaic (PV), wind turbine, inverter, and 40.5 kWh energy storage has a financial breakeven point after 9 years with wind turbine and 8 years without it. This implies that implementing wind turbines in the proposed building is not financially beneficial.Peer reviewe

Machine Learning Modeling for Energy Consumption of Residential and Commercial Sectors

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.Peer reviewe

Comparison of Common Monogenic Defects in a Large Predominantly Antibody Deficiency Cohort

Background: Predominantly antibody deficiencies (PADs) are the most common primary immunodeficiencies, characterized by hypogammaglobulinemia and inability to generate effective antibody responses. Objective: We intended to report most common monogenic PADs and to investigate how patients with PAD who were primarily diagnosed as suffering from agammaglobulinemia, hyper-IgM (HIgM) syndrome, and common variable immunodeficiency (CVID) have different clinical and immunological findings. Methods: Stepwise next-generation sequencing and Sanger sequencing were performed for confirmation of the mutations in the patients clinically diagnosed as suffering from agammaglobulinemia, HIgM syndrome, and CVID. Results: Among 550 registered patients, the predominant genetic defects associated with agammaglobulinemia (48 Bruton's tyrosine kinase [BTK] and 6 μ heavy chain deficiencies), HIgM syndrome (21 CD40 ligand and 7 activation-induced cytidine deaminase deficiencies), and CVID (17 lipopolysaccharides-responsive beige-like anchor deficiency and 12 atypical Immunodeficiency, Centromeric instability, and Facial dysmorphism syndromes) were identified. Clinical disease severity was significantly higher in patients with μ heavy chain and CD40 ligand mutations compared with patients with BTK (P = .003) and activation-induced cytidine deaminase (P = .009) mutations. Paralysis following live polio vaccination was considerably higher in patients with μ heavy chain deficiency compared with BTK deficiency (P < .001). We found a genotype-phenotype correlation among patients with BTK mutations regarding clinical manifestation of meningitis and chronic diarrhea. Surprisingly, we noticed that first presentations in most patients with Immunodeficiency, Centromeric instability, and Facial dysmorphism were respiratory complications (P = .008), whereas first presentations in patients with lipopolysaccharides-responsive beige-like anchor deficiency were nonrespiratory complications (P = .008). Conclusions: This study highlights similarities and differences in the clinical and genetic spectrum of the most common PAD-associated gene defects. This comprehensive comparison will facilitate clinical decision making, and improve prognosis and targeted treatment

Comparison of Common Monogenic Defects in a Large Predominantly Antibody Deficiency Cohort

Predominantly antibody deficiencies (PADs) are the most common primary immunodeficiencies, characterized by hypogammaglobulinemia and inability to generate effective antibody responses