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.

[1]  Yong Deng,et al.  Dependent Evidence Combination Based on Shearman Coefficient and Pearson Coefficient , 2018, IEEE Access.

[2]  Jianfeng Zhao,et al.  Speech emotion recognition using deep 1D & 2D CNN LSTM networks , 2019, Biomed. Signal Process. Control..

[3]  Hao Li,et al.  Photorealistic Facial Texture Inference Using Deep Neural Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[4]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[5]  Jing Zhao,et al.  A hybrid method of dynamic cooling and heating load forecasting for office buildings based on artificial intelligence and regression analysis , 2018, Energy and Buildings.

[6]  Junqiang Xi,et al.  Real-Time Energy Management Strategy Based on Velocity Forecasts Using V2V and V2I Communications , 2017, IEEE Transactions on Intelligent Transportation Systems.

[7]  Yuan Zhang,et al.  Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network , 2019, IEEE Transactions on Smart Grid.

[8]  Anders Malmquist,et al.  3E-Analysis of a Bio-Solar CCHP System for the Andaman Islands, India—A Case Study , 2019, Energies.

[9]  Christos Mousas,et al.  Evaluating the covariance matrix constraints for data-driven statistical human motion reconstruction , 2014, SCCG.

[10]  Yugang Niu,et al.  Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM , 2018 .

[11]  Christos Mousas,et al.  Generative Adversarial Network with Policy Gradient for Text Summarization , 2019, 2019 IEEE 13th International Conference on Semantic Computing (ICSC).

[12]  R. Vinayakumar,et al.  Automated detection of diabetes using CNN and CNN-LSTM network and heart rate signals , 2018 .

[13]  Dinggang Shen,et al.  State-space model with deep learning for functional dynamics estimation in resting-state fMRI , 2016, NeuroImage.

[14]  Priyanka Singh,et al.  Integration of new evolutionary approach with artificial neural network for solving short term load forecast problem , 2018 .

[15]  Mayur Barman,et al.  A regional hybrid GOA-SVM model based on similar day approach for short-term load forecasting in Assam, India , 2018 .

[16]  Juhan Nam,et al.  Learning Sparse Feature Representations for Music Annotation and Retrieval , 2012, ISMIR.

[17]  Ljupco Kocarev,et al.  Deep belief network based electricity load forecasting: An analysis of Macedonian case , 2016 .

[18]  Gerald Penn,et al.  Applying Convolutional Neural Networks concepts to hybrid NN-HMM model for speech recognition , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[19]  Christos Mousas,et al.  Learning Motion Features for Example-Based Finger Motion Estimation for Virtual Characters , 2017 .

[20]  Jiafu Wan,et al.  Industrial Big Data Analytics for Prediction of Remaining Useful Life Based on Deep Learning , 2018, IEEE Access.

[21]  Nilay Shah,et al.  A MINLP multi-objective optimization model for operational planning of a case study CCHP system in urban China , 2018 .

[22]  Chin-Hui Lee,et al.  An End-to-End Deep Learning Approach to Simultaneous Speech Dereverberation and Acoustic Modeling for Robust Speech Recognition , 2017, IEEE Journal of Selected Topics in Signal Processing.

[23]  Taku Komura,et al.  Segmentation of white matter hyperintensities using convolutional neural networks with global spatial information in routine clinical brain MRI with none or mild vascular pathology , 2018, Comput. Medical Imaging Graph..

[24]  Birgitte Bak-Jensen,et al.  A simplified short term load forecasting method based on sequential patterns , 2014, IEEE PES Innovative Smart Grid Technologies, Europe.

[25]  Jiangjiang Wang,et al.  Exergy and Exergoeconomic Analysis of a Combined Cooling, Heating, and Power System Based on Solar Thermal Biomass Gasification , 2019, Energies.

[26]  Jianhui Wang,et al.  Spatio-Temporal Graph Deep Neural Network for Short-Term Wind Speed Forecasting , 2019, IEEE Transactions on Sustainable Energy.

[27]  Gaurang Panchal,et al.  DETERMINATION OF OVER-LEARNING AND OVER-FITTING PROBLEM IN BACK PROPAGATION NEURAL NETWORK , 2011 .

[28]  He Jing,et al.  Deep convolutional neural networks for detecting secondary structures in protein density maps from cryo-electron microscopy , 2016 .

[29]  Yi Li,et al.  Flow Adversarial Networks: Flowrate Prediction for Gas–Liquid Multiphase Flows Across Different Domains , 2020, IEEE Transactions on Neural Networks and Learning Systems.

[30]  Christos Mousas,et al.  Dilated Convolutional Neural Network for Predicting Driver's Activity , 2018, 2018 21st International Conference on Intelligent Transportation Systems (ITSC).

[31]  Yudong Zhang,et al.  Multiple sclerosis identification by convolutional neural network with dropout and parametric ReLU , 2018, J. Comput. Sci..

[32]  Xu Chen,et al.  A hybrid teaching-learning artificial neural network for building electrical energy consumption prediction , 2018, Energy and Buildings.

[33]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[34]  Zheng-xin Wang,et al.  A seasonal GM(1,1) model for forecasting the electricity consumption of the primary economic sectors , 2018, Energy.

[35]  Moncef Gabbouj,et al.  Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks , 2017 .

[36]  Lin Fu,et al.  Summer performance analysis of coal-based CCHP with new configurations comparing with separate system , 2018 .

[37]  David A. Clausi,et al.  Sea Ice Concentration Estimation During Melt From Dual-Pol SAR Scenes Using Deep Convolutional Neural Networks: A Case Study , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[38]  Dino Isa,et al.  A load predictive energy management system for supercapacitor-battery hybrid energy storage system in solar application using the Support Vector Machine , 2015 .

[39]  Wan Kyun Chung,et al.  Super-High-Purity Seed Sorter Using Low-Latency Image-Recognition Based on Deep Learning , 2018, IEEE Robotics and Automation Letters.

[40]  Wang Xin,et al.  Short-term CHP heat load forecast method based on concatenated LSTMs , 2017, 2017 Chinese Automation Congress (CAC).

[41]  Sung-Bae Cho,et al.  Predicting residential energy consumption using CNN-LSTM neural networks , 2019, Energy.

[42]  Christos Mousas,et al.  Data-Driven Motion Reconstruction Using Local Regression Models , 2014, AIAI.

[43]  Flora Amato,et al.  Multimedia summarization using social media content , 2017, Multimedia Tools and Applications.

[44]  Hiroaki Nishi,et al.  Slightly-slacked dropout for improving neural network learning on FPGA , 2018, ICT Express.

[45]  Soumith Chintala,et al.  Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.

[46]  Jürgen Schmidhuber,et al.  Framewise phoneme classification with bidirectional LSTM and other neural network architectures , 2005, Neural Networks.

[47]  Taku Komura,et al.  Phase-functioned neural networks for character control , 2017, ACM Trans. Graph..

[48]  Juhan Nam,et al.  Multimodal Deep Learning , 2011, ICML.