Residential Appliance-Level Load Forecasting with Deep Learning

Razghandi, Mina; Turgut, Damla

doi:10.1109/globecom42002.2020.9348197

Cited by 15 publications

(6 citation statements)

References 9 publications

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“…As more predictions are made, more historical datasets (input sequences and the corresponding errors) become available, which can provide us with additional information to improve the further predictions. Many previous research works only consider adding different types of hidden layers (e.g., [10], [13]) to improve the performance, but none of them attempt to establish a learning process between the forecast error and the input features using historical data.…”

Section: B Lstm With Feedforward Controlmentioning

confidence: 99%

“…It allows for effectively designing incentive programs (e.g., [11]) and encouraging the active user participation in demand response. Residential appliance-level load forecasting has been recently investigated in [12], [13]. However, these proposed algorithms were developed for a very small number of households, and hence their generalizability to various types of appliances and households can come into question.…”

Section: Introductionmentioning

confidence: 99%

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Appliance Level Short-term Load Forecasting via Recurrent Neural Network

Zhou¹,

Nair²,

Ganger³

et al. 2021

Preprint

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Accurate load forecasting is critical for electricity market operations and other real-time decision-making tasks in power systems. This paper considers the short-term load forecasting (STLF) problem for residential customers within a community. Existing STLF work mainly focuses on forecasting the aggregated load for either a feeder system or a single customer, but few efforts have been made on forecasting the load at individual appliance level. In this work, we present an STLF algorithm for efficiently predicting the power consumption of individual electrical appliances. The proposed method builds upon a powerful recurrent neural network (RNN) architecture in deep learning, termed as long short-term memory (LSTM).As each appliance has uniquely repetitive consumption patterns, the patterns of prediction error will be tracked such that past prediction errors can be used for improving the final prediction performance. Numerical tests on real-world load datasets demonstrate the improvement of the proposed method over existing LSTM-based method and other benchmark approaches.

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Section: B Lstm With Feedforward Controlmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Appliance Level Short-term Load Forecasting via Recurrent Neural Network

Zhou¹,

Nair²,

Ganger³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Studies have found that LSTM models were among the best performing at residential load prediction. Turgut et al showed that LSTM models outperformed other models when forecasting appliance power such as the television and microwave [11]. The study in [12] also investigated LSTM models to predict total load and lighting for several different prediction windows or horizons.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Method for the Forecast and Separation of Total and HVAC Loads With Application to Energy Management of Smart and NZE Homes

et al. 2021

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“…Forecasting energy demand for appliances in a household whose use is largely determined by user preferences, such as washing machines, dishwashers, and other appliances, remains a challenge. Deep learning models, such as the Long Short-Term Memory (LSTM), are better suited to predicting appliance energy consumption during the day accurately using smart meter data [5]. More sophisticated techniques, such as sequence-to-sequence learning, can also be used to produce more reliable outcomes with fewer contextual data, such as outdoor temperature.…”

Section: Introductionmentioning

confidence: 99%

Smart Home Energy Management: Sequence-to-Sequence Load Forecasting and Q-Learning

Razghandi¹,

Zhang²,

Erol‐Kantarci³

et al. 2021

Preprint

Self Cite

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A smart home energy management system (HEMS) can contribute towards reducing the energy costs of customers; however, HEMS suffers from uncertainty in both energy generation and consumption patterns. In this paper, we propose a sequence to sequence (Seq2Seq) learning-based supply and load prediction along with reinforcement learning-based HEMS control. We investigate how the prediction method affects the HEMS operation. First, we use Seq2Seq learning to predict photovoltaic (PV) power and home devices' load. We then apply Q-learning for offline optimization of HEMS based on the prediction results. Finally, we test the online performance of the trained Q-learning scheme with actual PV and load data. The Seq2Seq learning is compared with VARMA, SVR, and LSTM in both prediction and operation levels. The simulation results show that Seq2Seq performs better with a lower prediction error and online operation performance.

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Residential Appliance-Level Load Forecasting with Deep Learning

Cited by 15 publications

References 9 publications

Appliance Level Short-term Load Forecasting via Recurrent Neural Network

Appliance Level Short-term Load Forecasting via Recurrent Neural Network

Artificial Intelligence Method for the Forecast and Separation of Total and HVAC Loads With Application to Energy Management of Smart and NZE Homes

Smart Home Energy Management: Sequence-to-Sequence Load Forecasting and Q-Learning

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