Short-term CHP heat load forecast method based on concatenated LSTMs

Lu, Kuan; Zeng, Bi; Wang, Xin; Meng, Xiangrong; Li, Honghai; Sun, Wei; Zhu, Zijian; Liu, Zhimin

doi:10.1109/cac.2017.8242744

Cited by 7 publications

(5 citation statements)

References 2 publications

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“…It effectively alleviates over-fitting by reducing the parameters between layers. According to the conclusion from the literature [24], the computationally efficient max pooling showed better results than other candidates, including average pooling and min pooling. The MaxPooling1D layer resizes it spatially and operates on every depth slice of the data.…”

Section: Conv1d Layer and Maxpooling1d Layermentioning

confidence: 92%

“…Indeed, the LSTM is good at dealing with time series with long time spans, which is suitable for forecasting short-time loads. Kuan Lu et al proposed a concatenated LSTM architecture for forecasting heating loads [24]. In order to solve the forecasting problem for the strong fluctuating household load, Weicong Kong et al improved the household prediction framework with automatic hyper parameter tuning based on LSTM network [14].…”

Section: Literature Reviewmentioning

confidence: 99%

“…The best choice was to use six LSTM layers. The neurons' number in each layer was 24,16,32,16,16, and 16, respectively. Both rates of the dropout were set to 0.25.…”

Section: Experimental Environment and Parametersmentioning

confidence: 99%

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Short-Term Load Forecasting for CCHP Systems Considering the Correlation between Heating, Gas and Electrical Loads Based on Deep Learning

2019

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Combined cooling, heating, and power (CCHP) systems is a distributed energy system that uses the power station or heat engine to generate electricity and useful heat simultaneously. Due to its wide range of advantages including efficiency, ecological, and financial, the CCHP will be the main direction of the integrated system. The accurate prediction of heating, gas, and electrical loads plays an essential role in energy management in CCHP systems. This paper combined long short-term memory (LSTM) network and convolutional neural network (CNN) to design a novel hybrid neural network for short-term loads forecasting considering their correlation. Pearson correlation coefficient will be utilized to measure the temporal correlation between current load and historical loads, and analyze the coupling between heating, gas and electrical loads. The dropout technique is proposed to solve the over-fitting of the network due to the lack of data diversity and network parameter redundancy. The case study shows that considering the coupling between heating, gas and electrical loads can effectively improve the forecasting accuracy, the performance of the proposed approach is better than that of the traditional methods.

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Section: Conv1d Layer and Maxpooling1d Layermentioning

confidence: 92%

Section: Literature Reviewmentioning

confidence: 99%

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Short-Term Load Forecasting for CCHP Systems Considering the Correlation between Heating, Gas and Electrical Loads Based on Deep Learning

2019

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“…By extracting the characteristics of the time series, the uncertainty of the model was reduced, and it was shown that the thermal load can be more effectively predicted in the long run. Kuan et al [17] used CHP's climate and heat load data in Shandong, China, to compare and compare existing LSTM (long short-term memory) techniques with high-density layers for two LSTM models. It showed that it converged to the optimal solution before LSTM.…”

Section: Deep Learning Applications In District Heating Systemsmentioning

confidence: 99%

“…In this study, we propose a heat production planning algorithm applying the deep learning technique, which has been successfully applied to various prediction and pattern recognition problems in real world applications [15][16][17][18][19][20][21]. The deep learning is a technique that learns patterns in the large-scale data that has both input and output values for a given problem and derives appropriate results according to the situation.…”

Section: Introductionmentioning

confidence: 99%

Applying Deep Learning to the Heat Production Planning Problem in a District Heating System

et al. 2020

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District heating system is designed to minimize energy consumption and environmental pollution by employing centralized production facilities connected to demand regions. Traditionally, optimization based algorithms were applied to the heat production planning problem in the district heating systems. Optimization-based models provide near optimal solutions, while it takes a while to generate solutions due to the characteristics of the underlying solution mechanism. When prompt re-planning due to any parameter changes is necessary, the traditional approaches might be inefficient to generate modified solutions quickly. In this study, we developed a two-phase solution mechanism, where deep learning algorithm is applied to learn optimal production patterns from optimization module. In the first training phase, the optimization module generates optimal production plans for the input scenarios derived from operations history, which are provided to the deep learning module for training. In the second planning phase, the deep learning module with trained parameters predicts production plan for the test scenarios. The computational experiments show that after the training process is completed, it has the characteristic of quickly deriving results appropriate to the situation. By combining optimization and deep learning modules in a solution framework, it is expected that the proposed algorithm could be applied to online optimization of district heating systems.

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Hourly Load Forecasting Using Sequence-to-Sequence LSTM-Based Deep Machine Learning Model

Majeed

Mudgal

Tak

et al. 2022

Sustainable Technology and Advanced Computing in Electrical Engineering

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Short-term CHP heat load forecast method based on concatenated LSTMs

Cited by 7 publications

References 2 publications

Short-Term Load Forecasting for CCHP Systems Considering the Correlation between Heating, Gas and Electrical Loads Based on Deep Learning

Short-Term Load Forecasting for CCHP Systems Considering the Correlation between Heating, Gas and Electrical Loads Based on Deep Learning

Applying Deep Learning to the Heat Production Planning Problem in a District Heating System

Hourly Load Forecasting Using Sequence-to-Sequence LSTM-Based Deep Machine Learning Model

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