Short Term Load Forecasting Using Deep Neural Networks

Mohammad, Faisal; Lee, Ki Boem; Kim, Young-Chon

doi:10.48550/arxiv.1811.03242

Cited by 1 publication

(1 citation statement)

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“…Deep Neural Networks (DNNs): Deep neural networks have been used to predict short-term load demand with high accuracy. To this end, a study by [18] showed that a DNN architecture, in this case Recurrent Deep Neural Network (Deep-RNN) with a tanh activation function, performs better than other cases in terms of MAPE metric values.…”

Section: Load Monitoringmentioning

confidence: 99%

Comparative Study on Load Monitoring Approaches

Tokam,

Ouro-Djobo

2023

Applied Sciences

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Without an appropriate monitoring system, the condition/state of electrical appliances/devices in operation in households cannot be fully assessed, resulting in uncontrolled expenses. The purpose of load monitoring techniques is to save electricity consumption. With proper controls, overconsumption of energy can be reduced and unwanted activity that can lead to unnecessary electricity consumption can be eliminated. To achieve this, two approaches are used. The first approach, which says that each device is monitored by means of individual meters or metering devices, is called intrusive load monitoring (ILM) and requires expensive deployment of metering devices for its use. In contrast to the first one, the second approach is non-intrusive load monitoring (NILM), which monitors electricity consumption without the need for any intrusion. In this configuration, the total energy consumed is disaggregated into the individual consumption of each load. With progress/advances in artificial intelligence, this approach is gaining interest with influences in other areas of research. Knowing that these developed techniques aim to encourage the occupants of dwellings to save energy by optimizing their electricity consumption, the paper presents a comparative study of these approaches, in order to highlight the strengths as well as the weaknesses of each of them. It is therefore a means of offering researchers the opportunity to make choices according to the orientations given to the research work.

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Section: Load Monitoringmentioning

confidence: 99%