Forecasting Electrical Demand for the Residential Sector at the National Level Using Deep Learning

2021 ◽  
Author(s):  
Pavan Kumar Dharmoju ◽  
Karthik Yeluripati ◽  
Jahnavi Guduri ◽  
Kowstubha Palle
Keyword(s):  
Deep Learning ◽  
National Level ◽  
Residential Sector ◽  
Electrical Demand

scholarly journals A Deep Learning Approach to Forecasting Monthly Demand for Residential–Sector Electricity

2020 ◽  
Vol 12 (8) ◽  
pp. 3103 ◽  
Author(s):  
Hyojoo Son ◽  
Changwan Kim
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Power System ◽  
Electricity Demand ◽  
Efficient Production ◽  
Power System Planning ◽  
System Planning ◽  
Residential Sector ◽  
Use Of Resources ◽  
Proposed Model

Forecasting electricity demand at the regional or national level is a key procedural element of power-system planning. However, achieving such objectives in the residential sector, the primary driver of peak demand, is challenging given this sector’s pattern of constantly fluctuating and gradually increasing energy usage. Although deep learning algorithms have recently yielded promising results in various time series analyses, their potential applicability to forecasting monthly residential electricity demand has yet to be fully explored. As such, this study proposed a forecasting model with social and weather-related variables by introducing long short-term memory (LSTM), which has been known to be powerful among deep learning-based approaches for time series forecasting. The validation of the proposed model was performed using a set of data spanning 22 years in South Korea. The resulting forecasting performance was evaluated on the basis of six performance measures. Further, this model’s performance was subjected to a comparison with the performance of four benchmark models. The performance of the proposed model was exceptional according to all of the measures employed. This model can facilitate improved decision-making regarding power-system planning by accurately forecasting the electricity demands of the residential sector, thereby contributing to the efficient production and use of resources.

scholarly journals Deep Learning Research: Scientometric Assessment of Global Publications Output during 2004 -17

2019 ◽  
Vol 3 (1) ◽  
pp. 23 ◽  
Author(s):  
B. M. Gupta ◽  
S. M. Dhawan
Keyword(s):  
Deep Learning ◽  
National Level ◽  
High Growth ◽  
Research Field ◽  
Qualitative Description ◽  
The Subject ◽  
Learning Research ◽  
Scientometric Assessment ◽  
Productive Authors ◽  
Highly Cited

The paper provides a quantitative and qualitative description of deep learning research using bibliometric indicators covering global research publications published during 14-year period 2004-17. Global deep learning research registered 106.76% high growth per annum, and averaged 7.99 citations per paper. Top 10 countries world- over dominate the research field with their 99.74% global publications share and more than 100% global citations share. China ranks the top with the highest (29.25%) global publications share, followed by USA (26.46%), U.K. (6.40%), etc. during the period. Canada tops in relative citation index (5.30). International collaboration has been a major driver of research in the subject with 14.96% to 53.76% of national-level share of top 10 countries output appeared as international collaborative publications. Computer Science is one of the most popular areas of research in deep learning research (76.85% share). The study identifies top 50 most productive organizations and 50 most productive authors and top 20 most productive journals reporting deep learning research and 118 highly cited papers with 100+ citations per paper.

scholarly journals Smart Energy Management for Unlocking Demand Response in the Residential Sector

Proceedings ◽  
2018 ◽  
Vol 2 (15) ◽  
pp. 1136 ◽  
Author(s):  
Xiangping Chen ◽  
Kui Weng ◽  
Fanlin Meng ◽  
Monjur Mourshed
Keyword(s):  
Energy Management ◽  
Demand Response ◽  
Energy Demand ◽  
Residential Sector ◽  
Cost Of Energy ◽  
Smart Energy Management ◽  
Electrical Demand ◽  
The Uk ◽  
The Cost ◽  
Pv Generation

This paper presents a smart energy management system for unlocking demand response in the UK residential sector. The approach comprises the estimation of one-hour energy demand and PV generation (supply) for scheduling the 24-h ahead demand profiles by shifting potential flexible loads. Real-time electrical demand is met by combining power supplies from PV, grid and batteries while minimizing consumer’s cost of energy. The results show that the peak-to-average ratio is reduced by 22.9% with the cost saving of 34.6% for the selected day.

scholarly journals Potential for Rooftop-Mounted PV Power Generation to Meet Domestic Electrical Demand in Saudi Arabia: Case Study of a Villa in Jeddah

Energies ◽  
2019 ◽  
Vol 12 (23) ◽  
pp. 4411 ◽  
Author(s):  
Abdulsalam S. Alghamdi
Keyword(s):  
Power Generation ◽  
Saudi Arabia ◽  
Solar Radiation ◽  
Wind Energy ◽  
Large Scale ◽  
National Level ◽  
Energy Resource ◽  
Electricity Consumption ◽  
The Government ◽  
Electrical Demand

The Kingdom of Saudi Arabia (KSA) has a large solar and wind energy resource. Through its Vision 2030 to exploit such resources, KSA is planning to install 9.5 GW of renewable energy power generation systems by 2030, through a mix of solar and wind energy. The government is planning to invest 109 billion US$ over the next 20 years for solar energy. The focus will be on solar photovoltaic (PV) and concentrated solar technologies at a national level. So far, the electricity demand in KSA is almost entirely dependent on fossil fuels for generating power. This paper addresses the potential to utilize the solar radiation resource at a different scale and reduce the power demand from the grid, bringing collateral benefits for householders and the government alike. The work presents the results from monitoring the electricity consumption of two typical domestic buildings (villas) in Jeddah, KSA. The electricity consumption observations were associated with indoor environmental conditions to study how and when cooling demand affects final demand. The study investigated options to serve the observed demand profile of the villas with simulated power generation from arrays of PV panels installed on two buildings’ roofs. Finally, a model of dynamic solar radiation simulation was developed to assess the hourly electricity generation, and a cost-benefit analysis was conducted for different capacity PV systems scenarios. The results indicate that locally used rooftop PV output could reduce the household electrical demand from the grid by around 80% at the housing level and in combination with building refurbishment solutions, could result in additional energy savings. The economic analysis discusses the implications of a proposed feed-in tariff with the associated payback periods and ROI, as well as proposals for PV system deployment at a large scale on the roof of buildings in KSA.

scholarly journals Effect of Subsidies and Tax Deductions on Promoting the Construction of Long-Life Quality Houses in Japan

2018 ◽  
Vol 15 (11) ◽  
pp. 2376 ◽  
Author(s):  
Ken’ichi Matsumoto ◽  
Yuki Yamamoto ◽  
Nao Ohya
Keyword(s):  
National Level ◽  
Life Quality ◽  
Economic Incentives ◽  
Housing Policies ◽  
Residential Sector ◽  
Tax Deductions ◽  
Policy Measures ◽  
Long Life ◽  
Positive Effect ◽  
Local Levels

Securing a quantity of houses for citizens has been the priority of housing policies in Japan. However, these policies shifted from quantity to quality in the 21st century, including the promotion of “long-life quality housing (LLQH)”, which contributes to a sustainable and healthy society for the residential sector. Since then, various policies have been introduced at the national and prefectural (local) levels to promote the construction of LLQH. Using panel data for 47 prefectures across seven years, this study aims to analyze the factors that Japanese households choose when constructing LLQH. Although various research on LLQH and similar housing exists, this study is the first attempt to empirically explore the factors that promote LLQH. We found that policy measures covering only LLQH were generally effective in promoting the construction of LLQH, and these policy measures were more effective than those covering both LLQH and other types of housing. National-level measures tended to be effective, whereas prefectural-level measures were not. Furthermore, although the effects of individual measures differed, the overall effects of policy measures were confirmed. In conclusion, providing economic incentives had a positive effect on promoting LLQH, and such measures were successful in achieving the intended purpose.

scholarly journals Citizen Participation in Low-Carbon Energy Systems: Energy Communities and Its Impact on the Electricity Demand on Neighborhood and National Level

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 305
Author(s):  
Sebastian Zwickl-Bernhard ◽  
Hans Auer
Keyword(s):  
Citizen Participation ◽  
Research Question ◽  
National Level ◽  
Energy Systems ◽  
Electricity Demand ◽  
Small Scale ◽  
Local Energy ◽  
Low Carbon ◽  
Residential Sector ◽  
Low Carbon Energy

In this work, the main research question is how a high penetration of energy communities (ECs) affects the national electricity demand in the residential sector. Thus, the existing building stock of three European regions/countries, namely, the Iberian Peninsula, Norway, and Austria, is analyzed and represented by four different model energy communities based on characteristic settlement patterns. A tailor-made, open-source model optimizes the utilization of the local energy technology portfolio, especially small-scale batteries and photovoltaic systems within the ECs. Finally, the results on the national level are achieved by upscaling from the neighborhood level. The findings of different 2030 scenarios (building upon narrative storylines), which consider various socio-economic and techno-economic determinants of possible future energy system development, identify a variety of modification potentials of the electricity demand as a result of EC penetration. The insights achieved in this work highlight the important contributions of ECs to low-carbon energy systems. Future work may focus on the provision of future local energy services, such as increasing cooling demand and/or high shares of electric vehicles, further enhancement of the upscaling to the national level (i.e., considering the distribution network capacities), and further diversification of EC composition beyond the residential sector.

Deep Learning

2009 ◽  
Author(s):  
Stellan Ohlsson
Keyword(s):  
Deep Learning
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