scholarly journals District Heating Load Prediction Algorithm Based on Feature Fusion LSTM Model

Energies ◽  
2019 ◽  
Vol 12 (11) ◽  
pp. 2122 ◽  
Author(s):  
Guixiang Xue ◽  
Yu Pan ◽  
Tao Lin ◽  
Jiancai Song ◽  
Chengying Qi ◽  
...  
Keyword(s):  
Prediction Accuracy ◽  
Feature Fusion ◽  
District Heating ◽  
Accurate Prediction ◽  
Prediction Algorithm ◽  
Load Prediction ◽  
Short Term ◽  
District Heating System ◽  
Heating Load ◽  
The Impact

The smart district heating system (SDHS) is an important element of the construction of smart cities in Northern China; it plays a significant role in meeting heating requirements and green energy saving in winter. Various Internet of Things (IoT) sensors and wireless transmission technologies are applied to monitor data in real-time and to form a historical database. The accurate prediction of heating loads based on massive historical datasets is the necessary condition and key basis for formulating an optimal heating control strategy in the SDHS, which contributes to the reduction in the consumption of energy and the improvement in the energy dispatching efficiency and accuracy. In order to achieve the high prediction accuracy of SDHS and to improve the representation ability of multi-time-scale features, a novel short-term heating load prediction algorithm based on a feature fusion long short-term memory (LSTM) model (FFLSTM) is proposed. Three characteristics, namely proximity, periodicity, and trend, are found after analyzing the heating load data from the aspect of the hourly time dimension. In order to comprehensively utilize the data’s intrinsic characteristics, three LSTM models are employed to make separate predictions, and, then, the prediction results based on internal features and other external features at the corresponding moments are imported into the high-level LSTM model for fusion processing, which brings a more accurate prediction result of the heating load. Detailed comparisons between the proposed FFLSTM algorithm and the-state-of-art algorithms are conducted in this paper. The experimental results show that the proposed FFLSTM algorithm outperforms others and can obtain a higher prediction accuracy. Furthermore, the impact of selecting different parameters of the FFLSTM model is also studied thoroughly.

Predicting Hourly Heating Load in a District Heating System Based on a Hybrid CNN-LSTM Model

2021 ◽  
pp. 110998
Author(s):  
Jiancai Song ◽  
Liyi Zhang ◽  
Guixiang Xue ◽  
YunPeng Ma ◽  
Shan Gao ◽  
...  
Keyword(s):  
District Heating ◽  
Heating System ◽  
District Heating System ◽  
Heating Load

scholarly journals Airbnb Short-term Housing Rental Status Prediction Model Under the Impact of the COVID-19 Pandemic

2021 ◽  
Vol 251 ◽  
pp. 01017
Author(s):  
Zhixiang Lu
Keyword(s):  
Machine Learning ◽  
New York ◽  
Regional Differences ◽  
Prediction Accuracy ◽  
Visual Analysis ◽  
Sharing Economy ◽  
Short Term ◽  
Emerging Industries ◽  
Machine Learning Model ◽  
The Impact

With the vigorous development of the sharing economy, the short-term rental industry has also spawned many emerging industries that belong to the sharing economy. However, due to the impact of the COVID-19 pandemic in 2020, many sharing economy industries, including the short-term housing leasing industry, have been affected. This study takes the rental information of 1,004 short-term rental houses in New York in April 2020 as an example, through machine learning and quantitative analysis, we conducted statistical and visual analysis on the impact of different factors on the housing rental status. This project is based on the machine learning model to predict the changes in the rental status of the house on the time series. The results show that the prediction accuracy of the random forest model has reached more than 94%, and the prediction accuracy of the logistic model has reached more than 74%. At the same time, we have further explored the impact of time span differences and regional differences on the housing rental status.

scholarly journals Data-Driven Machine-Learning Model in District Heating System for Heat Load Prediction: A Comparison Study

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Fisnik Dalipi ◽  
Sule Yildirim Yayilgan ◽  
Alemayehu Gebremedhin
Keyword(s):  
Machine Learning ◽  
Heat Load ◽  
District Heating ◽  
Heating System ◽  
Partial Least Square ◽  
Supervised Machine Learning ◽  
Data Driven ◽  
Support Vector ◽  
Load Prediction ◽  
District Heating System

We present our data-driven supervised machine-learning (ML) model to predict heat load for buildings in a district heating system (DHS). Even though ML has been used as an approach to heat load prediction in literature, it is hard to select an approach that will qualify as a solution for our case as existing solutions are quite problem specific. For that reason, we compared and evaluated three ML algorithms within a framework on operational data from a DH system in order to generate the required prediction model. The algorithms examined are Support Vector Regression (SVR), Partial Least Square (PLS), and random forest (RF). We use the data collected from buildings at several locations for a period of 29 weeks. Concerning the accuracy of predicting the heat load, we evaluate the performance of the proposed algorithms using mean absolute error (MAE), mean absolute percentage error (MAPE), and correlation coefficient. In order to determine which algorithm had the best accuracy, we conducted performance comparison among these ML algorithms. The comparison of the algorithms indicates that, for DH heat load prediction, SVR method presented in this paper is the most efficient one out of the three also compared to other methods found in the literature.

scholarly journals Research on Short-Term Load Prediction Based on Seq2seq Model

Energies ◽  
2019 ◽  
Vol 12 (16) ◽  
pp. 3199 ◽  
Author(s):  
Gangjun Gong ◽  
Xiaonan An ◽  
Nawaraj Kumar Mahato ◽  
Shuyan Sun ◽  
Si Chen ◽  
...  
Keyword(s):  
Power System ◽  
Prediction Model ◽  
Short Term Memory ◽  
Predictive Performance ◽  
Weather Conditions ◽  
Attention Mechanism ◽  
Load Prediction ◽  
Short Term ◽  
System Load ◽  
The Impact

Electricity load prediction is the primary basis on which power-related departments to make logical and effective generation plans and scientific scheduling plans for the most effective power utilization. The perpetual evolution of deep learning has recommended advanced and innovative concepts for short-term load prediction. Taking into consideration the time and nonlinear characteristics of power system load data and further considering the impact of historical and future information on the current state, this paper proposes a Seq2seq short-term load prediction model based on a long short-term memory network (LSTM). Firstly, the periodic fluctuation characteristics of users’ load data are analyzed, establishing a correlation of the load data so as to determine the model’s order in the time series. Secondly, the specifications of the Seq2seq model are given preference and a coalescence of the Residual mechanism (Residual) and the two Attention mechanisms (Attention) is developed. Then, comparing the predictive performance of the model under different types of Attention mechanism, this paper finally adopts the Seq2seq short-term load prediction model of Residual LSTM and the Bahdanau Attention mechanism. Eventually, the prediction model obtains better results when merging the actual power system load data of a certain place. In order to validate the developed model, the Seq2seq was compared with recurrent neural network (RNN), LSTM, and gated recurrent unit (GRU) algorithms. Last but not least, the performance indices were calculated. when training and testing the model with power system load data, it was noted that the root mean square error (RMSE) of Seq2seq was decreased by 6.61%, 16.95%, and 7.80% compared with RNN, LSTM, and GRU, respectively. In addition, a supplementary case study was carried out using data for a small power system considering different weather conditions and user behaviors in order to confirm the applicability and stability of the proposed model. The Seq2seq model for short-term load prediction can be reported to demonstrate superiority in all areas, exhibiting better prediction and stable performance.

scholarly journals The use of substations with PCM heat accumulators in district heating system

2018 ◽  
Vol 174 ◽  
pp. 01002 ◽  
Author(s):  
Kinga Nogaj ◽  
Michał Turski ◽  
Robert Sekret
Keyword(s):  
Phase Change Materials ◽  
District Heating ◽  
Heating System ◽  
Economic Benefits ◽  
District Heating System ◽  
Save Energy ◽  
Determination Of Parameters ◽  
Storage Technologies ◽  
The Impact

The main objective of the article is to indicate the directions of development of new generations of supplying buildings with heat, by using phase change materials, referring to the technical possibilities of applying available heat storage technologies. As a detailed objective of the work, the determination of the impact of using a PCM accumulator on the temperature of the heat carrier on the return in the substation of the district heating system was adopted. Range work included determination of parameters of heat distribution network as a function of outdoor air temperature range of -20°C to +12°C. As the analysis object, the heat substation has been selected with the following parameters: supply 80°C and return 60°C. It was found that thanks to the use of PCM accumulators on heat substations, it is possible to save energy by up to approx. 6.7% and achieve economic benefits in the form of a payback period of approx. 13 years. In addition, it was found that the introduction of the PCM accumulator into the heating system allows the return temperature in the heating network to be obtained at a temperature level consistent with the adopted control table for external temperatures of the standard heating season.

scholarly journals Prediction of heating load fluctuation based on fuzzy information granulation and support vector machine

2020 ◽  
pp. 307-307
Author(s):  
Tao Wang ◽  
Tingyu Ma ◽  
Dongsong Yan ◽  
Jing Song ◽  
Jianshuo Hu ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
District Heating ◽  
Series Data ◽  
Support Vector ◽  
Fuzzy Information ◽  
District Heating System ◽  
Information Granulation ◽  
Heating Load ◽  
Fuzzy Information Granulation

District heating systems are an important part of the future smart energy system and are seen as a tool to achieve energy efficiency goals in the EU. In order to achieve the real sense of heating on demand, based on historical heating load data, first of all, the heating load time series data was dealing with fuzzy information granulation, and then the cross-validation was used to explore the advantages of the data potential. Then the support vector machine regression prediction model was used for the prediction of the granulation data, finally, the heating load of a district heating system is simulated and verified. The simulation results show that the prediction model can effectively predict the trend of heating load, and provide a theoretical basis for the prediction of district heating load.

scholarly journals Air Quality Planning and the Minimization of Negative Externalities

Resources ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 15 ◽  
Author(s):  
Marco Ravina ◽  
Deborah Panepinto ◽  
Mariachiara Zanetti
Keyword(s):  
Air Quality ◽  
Urban Areas ◽  
District Heating ◽  
Heating System ◽  
Negative Externalities ◽  
District Heating System ◽  
Quality Planning ◽  
Air Quality Planning ◽  
The Impact

The minimization of negative externalities is a key aspect in the development of a circular and sustainable economic model. At the local scale, especially in urban areas, externalities are generated by the adverse impacts of air pollution on human health. Local air quality policies and plans often lack of considerations and instruments for the quantification and evaluation of external health costs. Support for decision-makers is needed, in particular during the implementation stage of air quality plans. Modelling tools based on the impact pathway approach can provide such support. In this paper, the implementation of health impacts and externalities analysis in air quality planning is evaluated. The state of the art in European member states is reported, considering whether and how health effects have been included in the planning schemes. The air quality plan of the Piemonte region in Italy is then considered. A case study is analyzed to evaluate a plan action, i.e., the development of the district heating system in the city of Turin. The DIATI (Dipartimento di Ingegneria dell’Ambiente, del Territorio e delle Infrastrutture) Dispersion and Externalities Model (DIDEM model) is applied to detect the scenario with the highest external cost reduction. This methodology results are extensible and adaptable to other actions and measures, as well as other local policies in Europe. The use of health externalities should be encouraged and integrated into the present methodology supporting air quality planning. Efforts should be addressed to quantify and minimize the overall uncertainty of the process.

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