Application of Deep Learning Long Short-Term Memory in Energy Demand Forecasting

Khafaf, Nameer Al; Jalili, Mahdi; Sokolowski, Peter

doi:10.1007/978-3-030-20257-6_3

Cited by 15 publications

(11 citation statements)

References 17 publications

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“…In recent years, researchers have used LSTM models widely with excellent results. However, the models lack the ability to learn features, requiring extensive feature engineering beforehand (Al Khafaf et al, 2019). Also, CNN's have been performing well in their predictions, though they have some difficulties in volatile consumption patterns .…”

Section: Resultsmentioning

confidence: 99%

A scoping review of deep neural networks for electric load forecasting

Vanting

Jôrgensen

2021

Energy Inform

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The increasing dependency on electricity and demand for renewable energy sources means that distributed system operators face new challenges in their grid. Accurate forecasts of electric load can solve these challenges. In recent years deep neural networks have become increasingly popular in research, and researchers have carried out many experiments to create the most accurate deep learning models. Players in the energy sector can exploit the increasing amount of energy-related data collected from smart meters to improve the grid’s operating quality. This review investigates state-of-the-art methodologies relating to energy load forecasting using deep neural networks. A thorough literature search is conducted, which outlines and analyses essential aspects regarding deep learning load forecasts in the energy domain. The literature suggests two main perspectives: demand-side management and grid control on the supply side. Each perspective has multiple applications with its challenges to achieve accurate forecasts; households, buildings, and grids. This paper recommends using a hybrid deep learning multivariate model consisting of a convolutional and recurrent neural network based on the scoping review. The suggested input variables should be historical consumption, weather, and day features. Combining the convolutional and recurrent networks ensures that the model learns as many repeating patterns and features in the data as possible.

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Section: Resultsmentioning

confidence: 99%

A scoping review of deep neural networks for electric load forecasting

Vanting

Jôrgensen

2021

Energy Inform

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“…Another example that highlight the performance of LSTM in energy forecasting is presented in [57] that forecasts 3-day ahead energy demand across each month in a year.…”

Section: A Forecasting Methodsmentioning

confidence: 99%

Deep Learning in Energy Modeling: Application in Smart Buildings With Distributed Energy Generation

et al. 2021

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Over 33% of final energy consumption is used in buildings which leads to nearly 40% of total direct and indirect CO 2 emissions in the world. While energy consumption is steadily rising globally, managing building energy utilization by on-site renewable energy generation can help responding this demand. This paper proposes a deep learning method based on a discrete wavelet transformation and long short-term memory method (DWT-LSTM) and a scheduling framework for the integrated modelling and management of energy demand and supply for buildings. This method considers several factors including a two-step electricity price, uncertainty in climatic factors, availability of renewable energy resources (wind and solar), energy consumption patterns in buildings, and the non-linear relationships between these parameters. This novel method analyzes and continuously learns from data patterns based on hourly, daily, weekly and monthly intervals. The method enables monitoring and controlling renewable energy generation, the share of energy imports from the grid, employment of saving strategy based on the user priority list, and energy storage management. The main objective of the proposed method is to minimize the reliance on the grid and electricity cost, especially during the peak days. The results demonstrate that the proposed method can forecast building energy demand and energy supply with a high level of accuracy, showing a 3.63-8.57% error range in hourly data prediction for one month ahead. The results also show that the method can supply 304 days (83.2%) of a year without reliance on energy grids, decreasing 87.2% in energy demand on one hand and exporting annually 7777 kWh to the grid on the other hand. In addition, the rescheduling framework decreased the imported electricity cost with the higher electricity tariff by 98 %. The combination of the deep learning forecasting, energy storage, and scheduling algorithm enables reducing annual energy import from the grid from 6709 to 858 kWh (84.3%).

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“…Somu and Ramamritham (2021) also predicted the future energy for a local building using combinations of CNN and LSTM. Al Khafaf et al (2019) used LSTM to forecast energy demands in Victoria, Australia. Cho (2019a, 2019b) also used LSTM and an auto-encounter for short-term forecasting of power demands in households.…”

Section: Energy Demand/consumption Forecastingmentioning

confidence: 99%

Incorporating causality in energy consumption forecasting using deep neural networks

2022

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Forecasting energy demand has been a critical process in various decision support systems regarding consumption planning, distribution strategies, and energy policies. Traditionally, forecasting energy consumption or demand methods included trend analyses, regression, and auto-regression. With advancements in machine learning methods, algorithms such as support vector machines, artificial neural networks, and random forests became prevalent. In recent times, with an unprecedented improvement in computing capabilities, deep learning algorithms are increasingly used to forecast energy consumption/demand. In this contribution, a relatively novel approach is employed to use long-term memory. Weather data was used to forecast the energy consumption from three datasets, with an additional piece of information in the deep learning architecture. This additional information carries the causal relationships between the weather indicators and energy consumption. This architecture with the causal information is termed as entangled long short term memory. The results show that the entangled long short term memory outperforms the state-of-the-art deep learning architecture (bidirectional long short term memory). The theoretical and practical implications of these results are discussed in terms of decision-making and energy management systems.

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Application of Deep Learning Long Short-Term Memory in Energy Demand Forecasting

Cited by 15 publications

References 17 publications

A scoping review of deep neural networks for electric load forecasting

A scoping review of deep neural networks for electric load forecasting

Deep Learning in Energy Modeling: Application in Smart Buildings With Distributed Energy Generation

Incorporating causality in energy consumption forecasting using deep neural networks

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