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

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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A short term load forecasting of integrated energy system based on CNN-LSTM

E3S Web of Conferences ◽

10.1051/e3sconf/202018501032 ◽

2020 ◽

Vol 185 ◽

pp. 01032

Author(s):

Xianjun Qi ◽

Xiwei Zheng ◽

Qinghui Chen

Keyword(s):

Neural Network ◽

Time Series ◽

Pearson Correlation ◽

Load Forecasting ◽

Energy System ◽

Composite Model ◽

Short Term ◽

Characteristic Quantity ◽

Integrated Energy System ◽

Coupling Characteristics

The accurate forecast of integrated energy loads, which has important practical significance, is the premise of the design, operation, scheduling and management of integrated energy systems. In order to make full use of the coupling characteristics of electricity, cooling and heating loads which is difficult to deal with by traditional methods, this paper proposes a new forecast model of integrated energy system loads based on the combination of convolutional neural network (CNN) and long short term memory (LSTM). Firstly, the Pearson correlation coefficients among the electricity, cooling and heating load series of the integrated energy system are calculated, and the results show that there is a strong coupling relationship between the loads of an integrated energy system. Then, the CNN-LSTM composite model is constructed, and CNN is used to extract the characteristic quantity which reflects the load coupling characteristics of the integrated energy system. Then, the characteristic quantity is converted into the time series input to LSTM, and the excellent time series processing ability of LSTM is used for load forecasting. The results show that the CNN-LSTM composite model proposed in this paper has higher prediction accuracy than the wavelet neural network model, CNN model and LSTM model.

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Short-term load prediction of integrated energy system based on neural network

Journal of Physics Conference Series ◽

10.1088/1742-6596/2087/1/012016 ◽

2021 ◽

Vol 2087 (1) ◽

pp. 012016

Author(s):

Yao Wang ◽

Xuxia Li ◽

Yan Liang ◽

Yingying Hu ◽

Xiaoming Zheng ◽

...

Keyword(s):

Neural Network ◽

Prediction Model ◽

Short Term Memory ◽

Energy System ◽

Load Prediction ◽

Short Term ◽

Nonlinear Characteristics ◽

Integrated Energy System ◽

Long Short Term Memory ◽

Grey Relation

Abstract Considering the correlation and nonlinear characteristics of multiple types of loads in the integrated energy system, grey relation analysis (GRA) and long short term Memory (LSTM) neural network are selected to establish the short-term load prediction model of the integrated energy system. The model uses GRA method to analyze the coupling between multiple types of loads and the meteorological factors, and then obtains the load forecast results through the LSTM prediction model. Finally, a practical example is given to verify the validity of the model.

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Estimation of municipal solid waste amount based on one-dimension convolutional neural network and long short-term memory with attention mechanism model: A case study of Shanghai

The Science of The Total Environment ◽

10.1016/j.scitotenv.2021.148088 ◽

2021 ◽

Vol 791 ◽

pp. 148088

Author(s):

Kunsen Lin ◽

Youcai Zhao ◽

Lu Tian ◽

Chunlong Zhao ◽

Meilan Zhang ◽

...

Keyword(s):

Neural Network ◽

Municipal Solid Waste ◽

Convolutional Neural Network ◽

Short Term Memory ◽

One Dimension ◽

Short Term ◽

Term Memory ◽

Mechanism Model ◽

Long Short Term Memory

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

Sustainability ◽

10.3390/su13010104 ◽

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.

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