Single Residential Load Forecasting Using Deep Learning and Image Encoding Techniques

Estebsari, Abouzar; Rajabi, Roozbeh

doi:10.3390/electronics9010068

Cited by 64 publications

(35 citation statements)

References 46 publications

(51 reference statements)

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“…However, installation and operation of storage units are quite costly for grid operators. In many cases, accurate load forecasting methods are proposed to capture the dynamic behaviour of demand in realtime and tune the prosumers accordingly [8], [9], [10], [11]. The new challenge in these cases is the controllability of RES.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Control of Power Exchange at Primary Substations: An OPF-Based Solution

Estebsari

Patti

Bottaccioli

2020

2020 IEEE International Conference on Environment and Electrical Engineering and 2020 IEEE Industrial and Commercial Power Syst

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Nowadays, integration of more renewable energy resources into distribution systems to inject more clean energy introduces new challenges to power system planning and operation. The intermittent behaviour of variable renewbale resources such as wind and PV generation would make the energy balancing more difficult, as current forecasting tools and existing storage units are insufficient. Transmission system operators may withstand some level of power imbalance, but fluctuations and noise of profiles are undesired. This requires local management performed or encouraged by distribution system operators. They could try to involve aggregators to exploit flexibility of loads through demand response schemes. In this paper, we present an optimal power flow-based algorithm written in Python which reads flexibility of different loads offered by the aggregators from one side, and the power flow deviation with respect to the scheduled profile at transmission-distribution coupling point from the other side, to define where and how much load to adjust. To demonstrate the applicability of this core, we setup a realtime simulation-based test bed and realised the performance of this approach in a real-like environment using real data of a network.

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Section: Introductionmentioning

confidence: 99%

Real-Time Control of Power Exchange at Primary Substations: An OPF-Based Solution

Estebsari

Patti

Bottaccioli

2020

2020 IEEE International Conference on Environment and Electrical Engineering and 2020 IEEE Industrial and Commercial Power Syst

Self Cite

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“…Furthermore, forecasting energy loads at such a granular level may be more challenging due to the uncertainty and volatility of the energy loads [81]. The types of DL-based papers applied at the residential level include: CNN [82], LSTM ( [83,84]), and comparison-based papers shown in papers [40,85,86]. [85].…”

Section: Residentialmentioning

confidence: 99%

A Review of Deep Learning Techniques for Forecasting Energy Use in Buildings

Runge

Zmeureanu

2021

Energies

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Buildings account for a significant portion of our overall energy usage and associated greenhouse gas emissions. With the increasing concerns regarding climate change, there are growing needs for energy reduction and increasing our energy efficiency. Forecasting energy use plays a fundamental role in building energy planning, management and optimization. The most common approaches for building energy forecasting include physics and data-driven models. Among the data-driven models, deep learning techniques have begun to emerge in recent years due to their: improved abilities in handling large amounts of data, feature extraction characteristics, and improved abilities in modelling nonlinear phenomena. This paper provides an extensive review of deep learning-based techniques applied to forecasting the energy use in buildings to explore its effectiveness and application potential. First, we present a summary of published literature reviews followed by an overview of deep learning-based definitions and techniques. Next, we present a breakdown of current trends identified in published research along with a discussion of how deep learning-based models have been applied for feature extraction and forecasting. Finally, the review concludes with current challenges faced and some potential future research directions.

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“…Although CNN is broadly used in image-related work, it is also used for probabilistic load forecasting [47], over-fitting issues [65], feature redundancy, and environment-friendly smart grid [77]. CNN-based encoded images have been applied for load forecasting [62].…”

Section: B Different Ann Techniques In Deep Learning Based Load Forementioning

confidence: 99%

HSIC Bottleneck Based Distributed Deep Learning Model for Load Forecasting in Smart Grid With a Comprehensive Survey

et al. 2020

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Load forecasting is a vital part of smart grids for predicting the required electrical power using artificial intelligence (AI). Deep learning is broadly used for load forecasting in the smart grid using the artificial neural network (ANN). Generally, computing the deep learning in the smart grid requires massive data aggregation or centralization and significant computational time. This paper presents a survey of deep learning-based load forecasting techniques from 2015 to 2020. This survey discusses the studies based on their deep learning techniques, Distributed Deep Learning (DDL) techniques, Back Propagation (BP) based works, and non-BP based works in the load forecasting process. Consequent to the survey, it was determined that data aggregation dependency would be beneficial for reducing computational time in load forecasting. Therefore, a conceptual model of DDL for smart grids has been presented, where the HSIC (Hilbert-Schmidt Independence Criterion) Bottleneck technique has been incorporated to provide higher accuracy.

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Single Residential Load Forecasting Using Deep Learning and Image Encoding Techniques

Cited by 64 publications

References 46 publications

Real-Time Control of Power Exchange at Primary Substations: An OPF-Based Solution

Real-Time Control of Power Exchange at Primary Substations: An OPF-Based Solution

A Review of Deep Learning Techniques for Forecasting Energy Use in Buildings

HSIC Bottleneck Based Distributed Deep Learning Model for Load Forecasting in Smart Grid With a Comprehensive Survey

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