A survey on deep learning methods for power load and renewable energy forecasting in smart microgrids

Aslam, Sheraz; Herodotou, Herodotos; Mohsin, Syed Muhammad; Javaid, Nadeem; Ashraf, Nouman; Aslam, Shahzad

doi:10.1016/j.rser.2021.110992

Cited by 340 publications

(139 citation statements)

References 108 publications

Supporting

Mentioning

139

Contrasting

Order By: Relevance

“…In an on-grid connection, an MG sends or receives power from the upstream grid and neighbor MGs in the area. Nevertheless, factors such as decreasing the power quality of the upstream grid based on the specific standards, significant disruption to the upstream grid, or maintenance program led to isolating the MG from the upstream grid [53][54][55][56].…”

Section: Introduction 1motivationsmentioning

confidence: 99%

Techno-Economic Planning and Operation of the Microgrid Considering Real-Time Pricing Demand Response Program

et al. 2021

Energies

Self Cite

View full text Add to dashboard Cite

The optimal planning of grid-connected microgrids (MGs) has been extensively studied in recent years. While most of the previous studies have used fixed or time-of-use (TOU) prices for the optimal sizing of MGs, this work introduces real-time pricing (RTP) for implementing a demand response (DR) program according to the national grid prices of Iran. In addition to the long-term planning of MG, the day-ahead operation of MG is also analyzed to get a better understanding of the DR program for daily electricity dispatch. For this purpose, four different days corresponding to the four seasons are selected for further analysis. In addition, various impacts of the proposed DR program on the MG planning results, including sizing and best configuration, net present cost (NPC) and cost of energy (COE), and emission generation by the utility grid, are investigated. The optimization results show that the implementation of the DR program has a positive impact on the technical, economic, and environmental aspects of MG. The NPC and COE are reduced by about USD 3700 and USD 0.0025/kWh, respectively. The component size is also reduced, resulting in a reduction in the initial cost. Carbon emissions are also reduced by 185 kg/year.

show abstract

Section: Introduction 1motivationsmentioning

confidence: 99%

Techno-Economic Planning and Operation of the Microgrid Considering Real-Time Pricing Demand Response Program

et al. 2021

Energies

Self Cite

View full text Add to dashboard Cite

show abstract

“…Here, an artificial neural network, due to its efficiency, is adopted to forecast the short term load in a day-ahead fashion. However, a few other algorithms to forecast the short and long term load are also being widely used by different authors [33]. In another similar approach, [34], the consumer's behaviours on each other's DSM decisions have been accounted for.…”

Section: Related Workmentioning

confidence: 99%

An Incentive Based Dynamic Pricing in Smart Grid: A Customer’s Perspective

et al. 2021

View full text Add to dashboard Cite

Price based demand response is an important strategy to facilitate energy retailers and end-users to maintain a balance between demand and supply while providing the opportunity to end users to get monetary incentives. In this work, we consider real-time electricity pricing policy to further calculate the incentives in terms of reduced electricity price and cost. Initially, a mathematical model based on the backtracking technique is developed to calculate the load shifted and consumed in any time slot. Then, based on this, the electricity price is calculated for all types of users to estimate the incentives through load shifting profiles. To keep the load under the upper limit, the load is shifted in other time slots in such a way to facilitate end-users regarding social welfare. The user who is not interested in participating load shifting program will not get any benefit. Then the well behaved functional form optimization problem is solved by using a heuristic-based genetic algorithm (GA), wwhich converged within an insignificant amount of time with the best optimal results. Simulation results reflect that the users can obtain some real incentives by participating in the load scheduling process.

show abstract

“…Due to the inconsistent, unpredictable, and irregular character of RE data, precise energy generation and consumption forecasting remains a difficult challenge. On this account, RE forecasting has been investigated in recent decades to address the issues that have arisen due to the significant increase in RES power plants around the world [10]. Different techniques for RE forecasting such as the future short-and long-term time intervals have been documented in the literature.…”

Section: Introductionmentioning

confidence: 99%

“…Due to the development and enhancement in the field of artificial intelligence (AI)based prediction models, machine learning (ML) and deep learning (DL) have proven to be successful tools for RE prediction. Research reveals that various ML and DL algorithms have been used for the purpose of RE forecasting [10]. Different assembled AI-based models have been developed to enhance the RE forecast accuracy [17].…”

Section: Introductionmentioning

confidence: 99%

AB-Net: A Novel Deep Learning Assisted Framework for Renewable Energy Generation Forecasting

et al. 2021

View full text Add to dashboard Cite

Renewable energy (RE) power plants are deployed globally because the renewable energy sources (RESs) are sustainable, clean, and environmentally friendly. However, the demand for power increases on a daily basis due to population growth, technology, marketing, and the number of installed industries. This challenge has raised a critical issue of how to intelligently match the power generation with the consumption for efficient energy management. To handle this issue, we propose a novel architecture called ‘AB-Net’: a one-step forecast of RE generation for short-term horizons by incorporating an autoencoder (AE) with bidirectional long short-term memory (BiLSTM). Firstly, the data acquisition step is applied, where the data are acquired from various RESs such as wind and solar. The second step performs deep preprocessing of the acquired data via several de-noising and cleansing filters to clean the data and normalize them prior to actual processing. Thirdly, an AE is employed to extract the discriminative features from the cleaned data sequence through its encoder part. BiLSTM is used to learn these features to provide a final forecast of power generation. The proposed AB-Net was evaluated using two publicly available benchmark datasets where the proposed method obtains state-of-the-art results in terms of the error metrics.

show abstract

A survey on deep learning methods for power load and renewable energy forecasting in smart microgrids

Cited by 340 publications

References 108 publications

Techno-Economic Planning and Operation of the Microgrid Considering Real-Time Pricing Demand Response Program

Techno-Economic Planning and Operation of the Microgrid Considering Real-Time Pricing Demand Response Program

An Incentive Based Dynamic Pricing in Smart Grid: A Customer’s Perspective

AB-Net: A Novel Deep Learning Assisted Framework for Renewable Energy Generation Forecasting

Contact Info

Product

Resources

About