scholarly journals Integrated energy system planning research based on big data load prediction method

2021 ◽  
Vol 267 ◽  
pp. 01005
Author(s):  
Yongli Wang ◽  
Hekun Shen ◽  
Jialin Yang ◽  
Nan Wang ◽  
Yuze Ma ◽  
...  
Keyword(s):  
Big Data ◽  
Energy Demand ◽  
Energy Use ◽  
Prediction Method ◽  
Energy System ◽  
System Capacity ◽  
Prediction Methods ◽  
Load Prediction ◽  
Related Factors ◽  
Integrated Energy System

The planning of integrated energy system is a very complex multi-target, multi-constraint, nonlinear, random uncertainty mixed integrated combination optimization problem, its planning and design process should not only consider the interdependence between the system capacity, energy conversion, energy storage, energy use and other links, but also consider the interaction and integration of cold, hot, electricity and other multi-energy flows, which is essentially a non-deterministic polynomial difficult problem. China’s energy continues to develop rapidly, all kinds of sensors and intelligent equipment data is increasing, the data obtained in the equipment and all kinds of sensors collected energy load prediction related factors such as temperature, weather, wind speed and other data volume increased dramatically, the data dimension is also increasing, the scale of data has also increased from GB to TB or even higher, based on the traditional prediction methods and intelligent prediction methods, has been far below the load forecast desired to achieve accuracy and speed requirements, Therefore, the use of big data technology to predict energy demand is an important future direction.

scholarly journals Location planning of distributed energy stations based on improved P-median model

2021 ◽  
Vol 256 ◽  
pp. 02009
Author(s):  
Zhengji Meng ◽  
Xiaoguang Hao ◽  
Shiyan Liu ◽  
Jianfeng Li
Keyword(s):  
Energy Demand ◽  
Energy Use ◽  
Transportation Network ◽  
Effective Means ◽  
Energy System ◽  
Weight Coefficient ◽  
Distributed Energy ◽  
Increase Energy Efficiency ◽  
Integrated Energy Systems ◽  
Integrated Energy System

The integrated energy system creates the possibility for the interconnection and coordination of different energy sources, and is an effective means to improve the energy use of the system, increase energy efficiency, and reduce environmental pollution. At present, the planning of distributed energy stations for integrated energy systems mostly focuses on equipment selection and equipment capacity. However, there are relatively few studies on the location of energy stations and pipeline layout planning. Firstly, this paper proposes a distributed energy station location method based on the improved p-median model, which combines the energy supply path of the energy station with the actual transportation network, and introduces the weight coefficient of multi energy load to reflect the diversity of energy demand of load. Finally, the specific solution method is given, and the rationality and feasibility of the proposed method are verified by an example.

scholarly journals Integrated energy system planning study based on load prediction

2021 ◽  
Vol 245 ◽  
pp. 01057
Author(s):  
Jialin Yang ◽  
Zhen Li ◽  
Nan Wang ◽  
Pengxiang Zhao ◽  
Xichao Zhou ◽  
...  
Keyword(s):  
Comprehensive Evaluation ◽  
System Modeling ◽  
Energy System ◽  
System Capacity ◽  
Load Prediction ◽  
System Planning ◽  
Planning And Design ◽  
Integrated Energy System ◽  
Energy System Planning ◽  
Whole Life Cycle

The planning of integrated energy system is a very complex multi-objective, multi-constraint, nonlinear, random uncertain hybrid combination optimization problem, its planning and design process should consider not only the system capacity, energy exchange, energy storage, energy and other links between the interdependence, but also the interaction and mixing of cold, hot, electricity and other multi-energy flow, which is essentially a non-deterministic polynomial problem. Based on load prediction technology, combined with scene generation, multi-interconnected energy system modeling and other technologies, around the integrated energy system planning and design, consider the comprehensive evaluation of the whole life cycle, an optimal configuration of the integrated energy system is formed.

Regional integrated energy system schemes selection based on risk expectation and Mahalanobis-Taguchi system

2021 ◽  
pp. 1-18
Author(s):  
Jiahang Yuan ◽  
Yun Li ◽  
Xinggang Luo ◽  
Lingfei Li ◽  
Zhongliang Zhang ◽  
...  
Keyword(s):  
Energy Demand ◽  
Choquet Integral ◽  
Subjective Evaluation ◽  
Risk Attitude ◽  
Energy System ◽  
Energy Utilization ◽  
Utilization Efficiency ◽  
Energy Structure ◽  
Fuzzy Measures ◽  
Integrated Energy System

Regional integrated energy system (RIES) provides a platform for coupling utilization of multi-energy and makes various energy demand from client possible. The suitable RIES composition scheme will upgrade energy structure and improve integrated energy utilization efficiency. Based on a RIES construction project in Jiangsu province, this paper proposes a new multi criteria decision-making (MCDM) method for the selection of RIES schemes. Because that subjective evaluation on RIES schemes benefit under criteria has uncertainty and hesitancy, intuitionistic trapezoidal fuzzy number (ITFN) which has the better capability to model ill-known quantities is presented. In consideration of risk attitude and interdependency of criteria, a new decision model with risk coefficients, Mahalanobis-Taguchi system and Choquet integral is proposed. Firstly, the decision matrices given by experts are normalized, and then are transformed to minimum expectation matrices according to different risk coefficients. Secondly, the weights of criteria from different experts are calculated by Mahalanobis-Taguchi system. Mobius transformation coefficients based on interaction degree are to calculate 2-order additive fuzzy measures, and then the comprehensive weights of criteria are obtained by fuzzy measures and Choquet integral. Thirdly, based on group decision consensus requirement, the weights of experts are obtained by the maximum entropy and grey correlation. Fourthly, the minimum expectation matrices are aggregated by the intuitionistic trapezoidal fuzzy Bonferroni mean operator. Thus, the ranking result according to the comparison rules using the minimum expectation and the maximum expectation is obtained. Finally, an illustrative example is taken in the present study to make the proposed method comprehensible.

Towards a 3D Spatial Urban Energy Modelling Approach

2014 ◽  
Vol 3 (3) ◽  
pp. 1-16 ◽  
Author(s):  
Jean-Marie Bahu ◽  
Andreas Koch ◽  
Enrique Kremers ◽  
Syed Monjur Murshed
Keyword(s):  
High Resolution ◽  
Distributed Generation ◽  
Energy Demand ◽  
Energy Use ◽  
Energy Systems ◽  
Energy System ◽  
Heat Energy ◽  
3D City Models ◽  
City Models ◽  
Modelling Approach

Today's needs to reduce the environmental impact of energy use impose dramatic changes for energy infrastructure and existing demand patterns (e.g. buildings) corresponding to their specific context. In addition, future energy systems are expected to integrate a considerable share of fluctuating power sources and equally a high share of distributed generation of electricity. Energy system models capable of describing such future systems and allowing the simulation of the impact of these developments thus require a spatial representation in order to reflect the local context and the boundary conditions. This paper describes two recent research approaches developed at EIFER in the fields of (a) geo-localised simulation of heat energy demand in cities based on 3D morphological data and (b) spatially explicit Agent-Based Models (ABM) for the simulation of smart grids. 3D city models were used to assess solar potential and heat energy demand of residential buildings which enable cities to target the building refurbishment potentials. Distributed energy systems require innovative modelling techniques where individual components are represented and can interact. With this approach, several smart grid demonstrators were simulated, where heterogeneous models are spatially represented. Coupling 3D geodata with energy system ABMs holds different advantages for both approaches. On one hand, energy system models can be enhanced with high resolution data from 3D city models and their semantic relations. Furthermore, they allow for spatial analysis and visualisation of the results, with emphasis on spatially and structurally correlations among the different layers (e.g. infrastructure, buildings, administrative zones) to provide an integrated approach. On the other hand, 3D models can benefit from more detailed system description of energy infrastructure, representing dynamic phenomena and high resolution models for energy use at component level. The proposed modelling strategies conceptually and practically integrate urban spatial and energy planning approaches. The combined modelling approach that will be developed based on the described sectorial models holds the potential to represent hybrid energy systems coupling distributed generation of electricity with thermal conversion systems.

scholarly journals Big Data in Smart Energy Systems: A Critical Review

2020 ◽  
Vol 11 (41) ◽  
pp. 11-26
Author(s):  
Keziban Seçkin Codal ◽  
İzzet Arı ◽  
H. Kemal İlter
Keyword(s):  
Big Data ◽  
Critical Review ◽  
Carbon Dioxide Emissions ◽  
Energy Demand ◽  
Renewable Energy Sources ◽  
Energy Systems ◽  
Energy System ◽  
Interdisciplinary Studies ◽  
Smart Energy Systems ◽  
The Impact

Climate change is an undeniable fact. Considering that two-thirds of greenhouse gas emissions originate from the energy sector, it is expected that the world's energy system will be transformed with renewable energy sources. Energy efficiency will be continuously increased. Reducing energy-related carbon dioxide emissions is the heart of the energy transition. Big data in energy systems play a crucial role in evaluating the adaptive capacity and investing more smartly to manage energy demand and supply. Indeed, the impact of the smart energy grid and meters on smart energy systems provide and assist decision-makers in transforming energy production, consumption, and communities. This study reviews the literature for aligning big data and smart energy systems and criticized according to regional perspective, period, disciplines, big data characteristics, and used data analytics. The critical review has been categorized into present themes. The results address issues, including scientific studies using data analysis techniques that take into account the characteristics of big data in the smart energy literature and the future of smart energy approaches. The manuscripts on big data in smart energy systems are a promising issue, albeit it is essential to expand subjects through comprehensive interdisciplinary studies

scholarly journals Electricity, Heat, and Gas Load Forecasting Based on Deep Multitask Learning in Industrial-Park Integrated Energy System

Entropy ◽  
2020 ◽  
Vol 22 (12) ◽  
pp. 1355 ◽  
Author(s):  
Linjuan Zhang ◽  
Jiaqi Shi ◽  
Lili Wang ◽  
Changqing Xu
Keyword(s):  
Energy Demand ◽  
Demand Forecasting ◽  
Energy System ◽  
Multitask Learning ◽  
Effective Characteristics ◽  
Industrial Park ◽  
Energy Forecasting ◽  
Training Stage ◽  
Integrated Energy System ◽  
Term Energy

Different energy systems are closely connected with each other in industrial-park integrated energy system (IES). The energy demand forecasting has important impact on IES dispatching and planning. This paper proposes an approach of short-term energy forecasting for electricity, heat, and gas by employing deep multitask learning whose structure is constructed by deep belief network (DBN) and multitask regression layer. The DBN can extract abstract and effective characteristics in an unsupervised fashion, and the multitask regression layer above the DBN is used for supervised prediction. Then, subject to condition of practical demand and model integrity, the whole energy forecasting model is introduced, including preprocessing, normalization, input properties, training stage, and evaluating indicator. Finally, the validity of the algorithm and the accuracy of the energy forecasts for an industrial-park IES system are verified through the simulations using actual operating data from load system. The positive results turn out that the deep multitask learning has great prospects for load forecast.

scholarly journals Optimal Planning of Integrated Energy Systems Based on Coupled CCHP

Energies ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 2621 ◽  
Author(s):  
Xiaofeng Dong ◽  
Chao Quan ◽  
Tong Jiang
Keyword(s):  
Power Generation ◽  
Energy Use ◽  
Clean Energy ◽  
Energy System ◽  
Power Lines ◽  
Gas Pipelines ◽  
Integrated Energy Systems ◽  
Integrated Energy System ◽  
Gas Price ◽  
Combined Cooling

With the widespread attention on clean energy use and energy efficiency, the integrated energy system (IES) has received considerable research and development. This paper proposed an electricity-gas IES optimization planning model based on a coupled combined cooling heating and power system (CCHP). The planning and operation of power lines and gas pipelines are considered. Regarding CCHP as the coupled hub of an electricity-gas system, the proposed model minimizes total cost in IES, with multistage planning and multi-scene analyzing. Renewable energy generation is also considered, including wind power generation and photovoltaic power generation. The numerical results reveal the replacing and adding schemes of power lines and gas pipelines, the optimal location and capacity of CCHP. In comparison with conventional separation production (SP), the optimization model which regards CCHP as the coupled hub attains better economy. At the same time, the influence of electricity price and natural gas price on the quantities of purchasing electricity and purchasing gas in the CCHP system is analyzed. According to the simulation result, a benchmark gas price is proposed, which shows whether the CCHP system chooses power generation. The model results and discussion demonstrate the validity of the model.

scholarly journals Digital Twin-Driven Decision Making and Planning for Energy Consumption

2021 ◽  
Vol 10 (2) ◽  
pp. 37
Author(s):  
Yasmin Fathy ◽  
Mona Jaber ◽  
Zunaira Nadeem
Keyword(s):  
Energy Consumption ◽  
Energy Demand ◽  
Energy Production ◽  
Energy Use ◽  
Synthetic Data ◽  
Average Energy ◽  
Energy System ◽  
Real Dataset ◽  
Digital Twin ◽  
The Impact

The Internet of Things (IoT) is revolutionising how energy is delivered from energy producers and used throughout residential households. Optimising the residential energy consumption is a crucial step toward having greener and sustainable energy production. Such optimisation requires a household-centric energy management system as opposed to a one-rule-fits all approach. In this paper, we propose a data-driven multi-layer digital twin of the energy system that aims to mirror households’ actual energy consumption in the form of a household digital twin (HDT). When linked to the energy production digital twin (EDT), HDT empowers the household-centric energy optimisation model to achieve the desired efficiency in energy use. The model intends to improve the efficiency of energy production by flattening the daily energy demand levels. This is done by collaboratively reorganising the energy consumption patterns of residential homes to avoid peak demands whilst accommodating the resident needs and reducing their energy costs. Indeed, our system incorporates the first HDT model to gauge the impact of various modifications on the household energy bill and, subsequently, on energy production. The proposed energy system is applied to a real-world IoT dataset that spans over two years and covers seventeen households. Our conducted experiments show that the model effectively flattened the collective energy demand by 20.9% on synthetic data and 20.4% on a real dataset. At the same time, the average energy cost per household was reduced by 10.7% for the synthetic data and 17.7% for the real dataset.

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