scholarly journals Energy Demand Forecast in Yunnan Province Based on Seq2Seq Model

2021 ◽  
Vol 293 ◽  
pp. 02063
Author(s):  
Anrui Li ◽  
Shi Su ◽  
Tong Han ◽  
Chunlin Yin ◽  
Jie Li ◽  
...  
Keyword(s):  
Energy Demand ◽  
Yunnan Province ◽  
Short Term Memory ◽  
Electricity Consumption ◽  
Forecast Model ◽  
Practical Significance ◽  
Demand Forecast ◽  
Total Energy Consumption ◽  
Coal Consumption ◽  
The Sustainable Development

Energy demand forecast has an important practical significance for the sustainable development of the national economy, the reasonable allocation of resources, and the construction of modernization goals. This study is based on the analysis of coal, electricity, natural gas, and other energy data in Yunnan Province from 2011 to 2018 and uses long short-term memory, sequence to sequence, deep learning, and ridge regression coupling methods to construct an energy demand forecast model in Yunnan Province. Forecast results show the following. The total energy consumption of Yunnan Province from 2021 to 2025 will continue to increase. Moreover, the coal consumption of Yunnan Province will continue to decline from 2021 to 2025. Furthermore, the electricity consumption of Yunnan will increase by about 8.02% year-on-year from 2021 to 2025. The experiment proves that the forecasting effect of the energy demand forecast model proposed in this study is excellent.

scholarly journals Establishment and prediction of energy demand model in Yunnan Province based on spatial nodes

2021 ◽  
Vol 293 ◽  
pp. 02061
Author(s):  
Liu Siyang ◽  
Wei Zirui ◽  
Qian Wen ◽  
Chen Yu ◽  
Liu Qian ◽  
...  
Keyword(s):  
Energy Demand ◽  
Yunnan Province ◽  
Model Simulation ◽  
Supply And Demand ◽  
Thermal Power ◽  
Electricity Consumption ◽  
Demand Model ◽  
Total Energy Consumption ◽  
Coal Consumption ◽  
Set Up

Energy demand is closely related to energy price, GDP and population. By using the shortest path algorithm and K-means clustering, we set up the spatial nodes, and carried out the model simulation to predict the energy demand of Yunnan Province. The results show that the total energy consumption of Yunnan Province will still show an upward trend from 2020 to 2015; hydropower silicon integration projects in Yunnan Province, the power supply and demand situation in Yunnan Province will change from oversupply to basic balance between supply and demand, and the role of thermal power in dry season will be played to make the decline of coal consumption tend to be smooth; from 2020 to 2025, Yunnan’s electricity consumption will increase by about 8.02% year-on-year. However, according to the commissioning of some projects, the total electricity consumption in the province will be about 192.9 billion kwh in 2020, with a yearon-year increase of 12.3%.

scholarly journals Determination of Electricity Demand by Personal Light Electric Vehicles (PLEVs): An Example of e-Motor Scooters in the Context of Large City Management in Poland

Energies ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 194 ◽  
Author(s):  
Anna Brdulak ◽  
Grażyna Chaberek ◽  
Jacek Jagodziński
Keyword(s):  
Sustainable Development ◽  
Electric Vehicles ◽  
Energy Demand ◽  
Large City ◽  
Electricity Consumption ◽  
City Management ◽  
The Sustainable Development ◽  
Dynamic Growth ◽  
Research Questions

Personal light electric vehicles (PLEVs) are a phenomenon that can currently be observed in cities, intended to be an ecological form of transport. The authors of the paper make an attempt to determine electricity consumption by PLEVs in the context of managing a large city in accordance with the concept of sustainable development. The article is of a cognitive nature. Research questions posed against the background of the goal formulated are as follows: how strong will the demand for PLEVs be (in the example of e-motor scooters, taking into consideration the number of vehicles) and for the electricity consumed by PLEVs. The method used is a simulation model. The conducted analyses demonstrate that a dynamic growth of PLEVs will result in an increased energy demand, which must be taken into account by the cities, developing according to the sustainable development conception.

scholarly journals Structural Analysis and Total Coal Demand Forecast in China

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Qing Zhu ◽  
Zhongyu Zhang ◽  
Rongyao Li ◽  
Kin Keung Lai ◽  
Shouyang Wang ◽  
...  
Keyword(s):  
Structural Changes ◽  
Thermal Power ◽  
Impulse Response Function ◽  
Forecast Model ◽  
Retail Trade ◽  
Industrial Output ◽  
Demand Forecast ◽  
Coal Consumption ◽  
Vector Autoregressive ◽  
Industry Output

Considering the speedy growth of industrialization and urbanization in China and the continued rise of coal consumption, this paper identifies factors that have impacted coal consumption in 1985–2011. After extracting the core factors, the Bayesian vector autoregressive forecast model is constructed, with variables that include coal consumption, the gross value of industrial output, and the downstream industry output (cement, crude steel, and thermal power). The impulse response function and variance decomposition are applied to portray the dynamic correlations between coal consumption and economic variables. Then for analyzing structural changes of coal consumption, the exponential smoothing model is also established, based on division of seven sectors. The results show that the structure of coal consumption underwent significant changes during the past 30 years. Consumption of both household sector and transport, storage, and post sectors continues to decline; consumption of wholesale and retail trade and hotels and catering services sectors presents a fluctuating and improving trend; and consumption of industry sector is still high. The gross value of industrial output and the downstream industry output have been promoting coal consumption growth for a long time. In 2015 and 2020, total coal demand is expected to reach 2746.27 and 4041.68 million tons of standard coal in China.

Current Hydropower Potential in Turkey and Sustainability of Hydropower for Turkey's Energy Demand

2005 ◽  
Vol 23 (1) ◽  
pp. 1-18 ◽  
Author(s):  
Mustafa Balat
Keyword(s):  
Energy Consumption ◽  
Energy Demand ◽  
Electricity Consumption ◽  
Power Markets ◽  
Total Energy Consumption ◽  
Rapid Urbanization ◽  
Public Company ◽  
Hydropower Potential ◽  
Hydroelectric Plants ◽  
Per Capita

With a young and growing population, low per capita electricity consumption, rapid urbanization and strong economic growth, Turkey for nearly two decades has been one of the fastest growing power markets in the world. Domestic energy consumption accounts for 37% of total energy consumption. For this reason, the renewable sources are very important for Turkey's energy sector. Projections by Turkey's Electricity Generating and Transmission Corporation (TEAS), a public company which owns and operates 15 thermal and 30 hydroelectric plants generating 91% of Turkey's electricity, indicate that rapid (as high as 10% annual) growth in electricity consumption will continue over the next 15 years. Turkey has a total gross hydropower potential of 433 GW, but only 125 GW of the total hydroelectric potential of Turkey can be economically used.

scholarly journals Energy Consumption and GGP in the OECD Countries: A Causality Analysis

2017 ◽  
Vol 10 (1) ◽  
pp. 55-74 ◽  
Author(s):  
Mondiu T. Jaiyesimi ◽  
Tokunbo S. Osinubi ◽  
Lloyd Amaghionyeodiwe
Keyword(s):  
Energy Consumption ◽  
Total Energy ◽  
Energy Demand ◽  
Generalized Method Of Moments ◽  
Electricity Consumption ◽  
Secondary Data ◽  
Least Square ◽  
Causality Analysis ◽  
Demand Model ◽  
Total Energy Consumption

Abstract This study investigated the nature or direction of causality between GDP, electricity consumption and total energy consumption in the OECD. Secondary data was used while both the ordinary least square (OLS) and generalized method of moments (GMM) estimators were employed to test for causality in our model. Our result found the presence of a bi-directional causality between energy consumption and GDP for the total energy demand model and between electricity consumption and GDP for the electricity demand model. By implication, the bi-directional causality in our estimated models suggest that both energy consumption and GDP are important factors in economic development in the OECD. Thus, if misguided policy measures are made to reduce energy consumption it could have a detrimental effect on GDP which will slow down economic growth. A recommendation is for policy makers to concentrate on encouraging energy efficiency as a way to reduce energy and electricity consumption.

scholarly journals Is Urbanisation Rate a Feasible Supplemental Parameter in Forecasting Electricity Consumption in China?

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Biao Yang ◽  
Yingcheng Li ◽  
Haokun Wei ◽  
Huan Lu
Keyword(s):  
Regression Models ◽  
Electricity Consumption ◽  
Forecast Model ◽  
Electricity Demand ◽  
Shaanxi Province ◽  
Average Error ◽  
Demand Forecast ◽  
Valid Predictor ◽  
The Government

Traditional method of forecasting electricity consumption based only on GDP was sometimes ineffective. In this paper, urbanisation rate (UR) was introduced as an additional predictor to improve the electricity demand forecast in China at provincial scale, which was previously based only on GDP. Historical data of Shaanxi province from 2000 to 2013 was collected and used as case study. Four regression models were proposed and GDP, UR, and electricity consumption (EC) were used to establish the parameters in each model. The model with least average error of hypothetical forecast results in the latest three years was selected as the optimal forecast model. This optimal model divides total EC into four parts, of which forecasts can be made separately. It was found that GDP was only better correlated than UR on household EC, whilst UR was better on the three sectors of industries. It was concluded that UR is a valid predictor to forecast electricity demand at provincial level in China nowadays. Being provided the planned value of GDP and UR from the government, EC in 2015 were forecasted as 131.3 GWh.

scholarly journals Electricity Consumption Forecasting Based on a Bidirectional Long-Short-Term Memory Artificial Neural Network

2020 ◽  
Vol 13 (1) ◽  
pp. 104
Author(s):  
Dana-Mihaela Petroșanu ◽  
Alexandru Pîrjan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Short Term Memory ◽  
Electricity Consumption ◽  
Short Term ◽  
Term Memory ◽  
Storage Room ◽  
Long Short Term Memory ◽  
Commercial Center ◽  
Business And Management

The accurate forecasting of the hourly month-ahead electricity consumption represents a very important aspect for non-household electricity consumers and system operators, and at the same time represents a key factor in what regards energy efficiency and achieving sustainable economic, business, and management operations. In this context, we have devised, developed, and validated within the paper an hourly month ahead electricity consumption forecasting method. This method is based on a bidirectional long-short-term memory (BiLSTM) artificial neural network (ANN) enhanced with a multiple simultaneously decreasing delays approach coupled with function fitting neural networks (FITNETs). The developed method targets the hourly month-ahead total electricity consumption at the level of a commercial center-type consumer and for the hourly month ahead consumption of its refrigerator storage room. The developed approach offers excellent forecasting results, highlighted by the validation stage’s results along with the registered performance metrics, namely 0.0495 for the root mean square error (RMSE) performance metric for the total hourly month-ahead electricity consumption and 0.0284 for the refrigerator storage room. We aimed for and managed to attain an hourly month-ahead consumed electricity prediction without experiencing a significant drop in the forecasting accuracy that usually tends to occur after the first two weeks, therefore achieving a reliable method that satisfies the contractor’s needs, being able to enhance his/her activity from the economic, business, and management perspectives. Even if the devised, developed, and validated forecasting solution for the hourly consumption targets a commercial center-type consumer, based on its accuracy, this solution can also represent a useful tool for other non-household electricity consumers due to its generalization capability.

scholarly journals Impact of Passive Energy Efficiency Measures on Cooling Energy Demand in an Architectural Campus Building in Karachi, Pakistan

2021 ◽  
Vol 13 (13) ◽  
pp. 7251
Author(s):  
Mushk Bughio ◽  
Muhammad Shoaib Khan ◽  
Waqas Ahmed Mahar ◽  
Thorsten Schuetze
Keyword(s):  
Energy Efficiency ◽  
Energy Demand ◽  
Electricity Consumption ◽  
Information Modeling ◽  
Base Case ◽  
Energy Efficiency Measures ◽  
Efficiency Measures ◽  
The Impact ◽  
Cooling Energy Demand ◽  
Campus Building

Electric appliances for cooling and lighting are responsible for most of the increase in electricity consumption in Karachi, Pakistan. This study aims to investigate the impact of passive energy efficiency measures (PEEMs) on the potential reduction of indoor temperature and cooling energy demand of an architectural campus building (ACB) in Karachi, Pakistan. PEEMs focus on the building envelope’s design and construction, which is a key factor of influence on a building’s cooling energy demand. The existing architectural campus building was modeled using the building information modeling (BIM) software Autodesk Revit. Data related to the electricity consumption for cooling, building masses, occupancy conditions, utility bills, energy use intensity, as well as space types, were collected and analyzed to develop a virtual ACB model. The utility bill data were used to calibrate the DesignBuilder and EnergyPlus base case models of the existing ACB. The cooling energy demand was compared with different alternative building envelope compositions applied as PEEMs in the renovation of the existing exemplary ACB. Finally, cooling energy demand reduction potentials and the related potential electricity demand savings were determined. The quantification of the cooling energy demand facilitates the definition of the building’s electricity consumption benchmarks for cooling with specific technologies.

scholarly journals Control Strategies for Daylight and Artificial Lighting in Office Buildings—A Bibliometrically Assisted Review

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3852
Author(s):  
Daniel Plörer ◽  
Sascha Hammes ◽  
Martin Hauer ◽  
Vincent van Karsbergen ◽  
Rainer Pfluger
Keyword(s):  
Energy Efficiency ◽  
Energy Demand ◽  
Control Strategies ◽  
Significant Proportion ◽  
Decisive Role ◽  
Future Trend ◽  
Office Buildings ◽  
Total Energy Consumption ◽  
Integral Control ◽  
Artificial Lighting

A significant proportion of the total energy consumption in office buildings is attributable to lighting. Enhancements in energy efficiency are currently achieved through strategies to reduce artificial lighting by intelligent daylight utilization. Control strategies in the field of daylighting and artificial lighting are mostly rule-based and focus either on comfort aspects or energy objectives. This paper aims to provide an overview of published scientific literature on enhanced control strategies, in which new control approaches are critically analysed regarding the fulfilment of energy efficiency targets and comfort criteria simultaneously. For this purpose, subject-specific review articles from the period between 2015 and 2020 and their research sources from as far back as 1978 are analysed. Results show clearly that building controls increasingly need to address multiple trades to achieve a maximum improvement in user comfort and energy efficiency. User acceptance can be highlighted as a decisive factor in achieving targeted system efficiencies, which are highly determined by the ability of active user interaction in the automatic control system. The future trend is moving towards decentralized control concepts including appropriate occupancy detection and space zoning. Simulation-based controls and learning systems are identified as appropriate methods that can play a decisive role in reducing building energy demand through integral control concepts.

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