Related papers: Deep Convolutional Neural Networks for Short-Term Multi-Energy Demand Prediction of Integrated Energy Systems

Deep Convolutional Neural Networks for Short-Term Multi-Energy Demand Prediction of Integrated Energy Systems

URL: http://arxiv.org/abs/2312.15497v1
Date: Sun, 24 Dec 2023 14:56:23 GMT
Title: Deep Convolutional Neural Networks for Short-Term Multi-Energy Demand Prediction of Integrated Energy Systems
Authors: Corneliu Arsene
Abstract summary: This paper develops six novel prediction models based on Convolutional Neural Networks (CNNs) for forecasting multi-energy power consumptions. The models are applied in a comprehensive manner on a novel integrated electrical, heat and gas network system.
Score: 0.0
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Forecasting power consumptions of integrated electrical, heat or gas network systems is essential in order to operate more efficiently the whole energy network. Multi-energy systems are increasingly seen as a key component of future energy systems, and a valuable source of flexibility, which can significantly contribute to a cleaner and more sustainable whole energy system. Therefore, there is a stringent need for developing novel and performant models for forecasting multi-energy demand of integrated energy systems, which to account for the different types of interacting energy vectors and of the coupling between them. Previous efforts in demand forecasting focused mainly on the single electrical power consumption or, more recently, on the single heat or gas power consumptions. In order to address this gap, in this paper six novel prediction models based on Convolutional Neural Networks (CNNs) are developed, for either individual or joint prediction of multi-energy power consumptions: the single input/single output CNN model with determining the optimum number of epochs (CNN_1), the multiple input/single output CNN model (CNN_2), the single input/ single output CNN model with training/validation/testing datasets (CNN_3), the joint prediction CNN model (CNN_4), the multiple-building input/output CNN model (CNN_5) and the federated learning CNN model (CNN_6). All six novel CNN models are applied in a comprehensive manner on a novel integrated electrical, heat and gas network system, which only recently has started to be used for forecasting. The forecast horizon is short-term (next half an hour) and all the predictions results are evaluated in terms of the Signal to Noise Ratio (SNR) and the Normalized Root Mean Square Error (NRMSE), while the Mean Absolute Percentage Error (MAPE) is used for comparison purposes with other existent results from literature.

Related papers

Clustering-based Multitasking Deep Neural Network for Solar Photovoltaics Power Generation Prediction [16.263501526929975]
We propose a multitasking deep neural network (CM-DNN) framework for PV power generation prediction. For each type, a deep neural network (DNN) is employed and trained until the accuracy cannot be improved. For a specified customer type, inter-model knowledge transfer is conducted to enhance its training accuracy. The proposed CM-DNN is tested on a real-world PV power generation dataset.
arXiv Detail & Related papers (2024-05-09T00:08:21Z)
MSDC: Exploiting Multi-State Power Consumption in Non-intrusive Load Monitoring based on A Dual-CNN Model [18.86649389838833]
Non-intrusive load monitoring (NILM) aims to decompose aggregated electrical usage signal into appliance-specific power consumption. We design a new neural NILM model Multi-State Dual CNN (MSDC) MSDC explicitly extracts information about the appliance's multiple states and state transitions, which in turn regulates the prediction of signals for appliances.
arXiv Detail & Related papers (2023-02-11T01:56:54Z)
Fluid Batching: Exit-Aware Preemptive Serving of Early-Exit Neural Networks on Edge NPUs [74.83613252825754]
"smart ecosystems" are being formed where sensing happens concurrently rather than standalone. This is shifting the on-device inference paradigm towards deploying neural processing units (NPUs) at the edge. We propose a novel early-exit scheduling that allows preemption at run time to account for the dynamicity introduced by the arrival and exiting processes.
arXiv Detail & Related papers (2022-09-27T15:04:01Z)
EVE: Environmental Adaptive Neural Network Models for Low-power Energy Harvesting System [8.16411986220709]
Energy harvesting technology that harvests energy from ambient environment is a promising alternative to batteries for powering those devices. This paper proposes EVE, an automated machine learning framework to search for desired multi-models with shared weights for energy harvesting IoT devices. Experimental results show that the neural networks models generated by EVE is on average 2.5X faster than the baseline models without pruning and shared weights.
arXiv Detail & Related papers (2022-07-14T20:53:46Z)
Cascaded Deep Hybrid Models for Multistep Household Energy Consumption Forecasting [5.478764356647437]
This study introduces two hybrid cascaded models for forecasting multistep household power consumption in different resolutions. The proposed hybrid models achieve superior prediction performance compared to the existing multistep power consumption prediction methods.
arXiv Detail & Related papers (2022-07-06T11:02:23Z)
Pretraining Graph Neural Networks for few-shot Analog Circuit Modeling and Design [68.1682448368636]
We present a supervised pretraining approach to learn circuit representations that can be adapted to new unseen topologies or unseen prediction tasks. To cope with the variable topological structure of different circuits we describe each circuit as a graph and use graph neural networks (GNNs) to learn node embeddings. We show that pretraining GNNs on prediction of output node voltages can encourage learning representations that can be adapted to new unseen topologies or prediction of new circuit level properties.
arXiv Detail & Related papers (2022-03-29T21:18:47Z)
Enhanced physics-constrained deep neural networks for modeling vanadium redox flow battery [62.997667081978825]
We propose an enhanced version of the physics-constrained deep neural network (PCDNN) approach to provide high-accuracy voltage predictions. The ePCDNN can accurately capture the voltage response throughout the charge--discharge cycle, including the tail region of the voltage discharge curve.
arXiv Detail & Related papers (2022-03-03T19:56:24Z)
Demand Forecasting in Smart Grid Using Long Short-Term Memory [0.0]
Long Short-Term Memory (LSTM) shows promising results in predicting time series data. In this paper, an LSTM based model using neural network architecture is proposed to forecast power demand.
arXiv Detail & Related papers (2021-07-28T21:45:54Z)
Energy-Efficient Model Compression and Splitting for Collaborative Inference Over Time-Varying Channels [52.60092598312894]
We propose a technique to reduce the total energy bill at the edge device by utilizing model compression and time-varying model split between the edge and remote nodes. Our proposed solution results in minimal energy consumption and $CO$ emission compared to the considered baselines.
arXiv Detail & Related papers (2021-06-02T07:36:27Z)
Risk-Aware Energy Scheduling for Edge Computing with Microgrid: A Multi-Agent Deep Reinforcement Learning Approach [82.6692222294594]
We study a risk-aware energy scheduling problem for a microgrid-powered MEC network. We derive the solution by applying a multi-agent deep reinforcement learning (MADRL)-based advantage actor-critic (A3C) algorithm with shared neural networks.
arXiv Detail & Related papers (2020-02-21T02:14:38Z)
Model Fusion via Optimal Transport [64.13185244219353]
We present a layer-wise model fusion algorithm for neural networks. We show that this can successfully yield "one-shot" knowledge transfer between neural networks trained on heterogeneous non-i.i.d. data.
arXiv Detail & Related papers (2019-10-12T22:07:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.