Peak Load Forecasting Based on Long Short Term Memory

Ermatita, Ermatita; Pahendra, Iwan; Darnila, Eva; Sadli, Muhammad; Sinambela, Marzuki; Fuadi, Wahyu

doi:10.1109/icimcis48181.2019.8985197

Cited by 5 publications

(6 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many modern and advanced methods have emerged based on ANN, such as self-organizing map (SOM), recurrent neural network (RNN), and convolution neural network (CNN). In particular, long-short memory (LSTM) is one variation of RNN [93].…”

Section: Artificial Neural Network and Deep Learning-based Methodsmentioning

confidence: 99%

Electrical peak demand forecasting- A review

Dai¹,

Meng²,

Dai³

et al. 2021

Preprint

View full text Add to dashboard Cite

The power system is undergoing rapid evolution with the roll-out of advanced metering infrastructure and local energy applications (e.g. electric vehicles) as well as the increasing penetration of intermittent renewable energy at both transmission and distribution level, which characterizes the peak load demand with stronger randomness and less predictability and therefore poses a threat to the power grid security. Since storing large quantities of electricity to satisfy load demand is neither economically nor environmentally friendly, effective peak demand management strategies and reliable peak load forecast methods become essential for optimizing the power system operations. To this end, this paper provides a timely and comprehensive overview of peak load demand forecast methods in the literature. To our best knowledge, this is the first comprehensive review on such topic. In this paper we first give a precise and unified problem definition of peak load demand forecast. Second, 139 papers on peak load forecast methods were systematically reviewed where methods were classified into different stages based on the timeline. Thirdly, a comparative analysis of peak load forecast methods are summarized and different optimizing methods to improve the forecast performance are discussed. The paper ends with a comprehensive summary of the reviewed papers and a discussion of potential future research directions.

show abstract

Section: Artificial Neural Network and Deep Learning-based Methodsmentioning

confidence: 99%

Electrical peak demand forecasting- A review

Dai¹,

Meng²,

Dai³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…As a type of recurrent neural network, LSTM can learn the order dependence between items in a sequence. It also had the potential of being able to learn the context required to make predictions in time series forecasting problems, rather than having this context pre-specified and fixed (Ermatita et al, 2019;Fallah, Ganjkhani, Shamshirband, & Chau, 2019 Halaman 34…”

Section: Literature Reviewmentioning

confidence: 99%

Time Series Forecasting for Average Temperature in 96041 Station Using Long Short-Term Memory Model

Damanik

Pane

Dewi

et al. 2021

jmika

Self Cite

View full text Add to dashboard Cite

An understanding of patterns and gauge of normal temperature joined of parameter climate and climate information for way better water asset administration and arranging during a bowl is exceptionally vital. Investigate climate patterns utilizing typical and neighborhood annually normal temperatures, compare and make perceptions. during this consider, we'll analyze nearby and ordinary normal temperature information in 96041 Station supported perception station in place. the foremost objective of this considers to seem the execution of the traditional temperature in an exceedingly single station and to foresee the conventional temperature information utilizing the Long memory Demonstrate approach. supported the results of ordinary informatics of investigating temperature with nearby temperature relationship, we got the show of preparing bend, remaining plot, and therefore the diffuse plot is appeared utilizing these codes. the nice execution of 96041 Station had an Mean Squared Error esteem of 0.01 and R squared esteem 0.98, concerning zero will speak to superior quality of the indicator.

show abstract

“…This paper concerns 1 week ahead of daily peak load forecasting (MTLF). Load forecasting methods can be classified into statistical methods [1][2][3] and machine learning methods [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18]. Recently, deep learning algorithms have also received much attention [12][13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

“…Kwon concatenated the long short-term memory (LSTM) layer with the fully-connected (FC) layer to yield a deep learning algorithm for weekly peak load forecasting [17]. Ermatita et al predicted future monthly electricity peak load requirements from available data using LSTM [18]. Tang et al presented a multi-layer bidirectional RNN model based on LSTM and gated recurrent unit (GRU) to forecast daily peak load on two data sets [19].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Week‐ahead daily peak load forecasting using genetic algorithm‐based hybrid convolutional neural network

Hong

Chan

Cheng

et al. 2022

IET Generation Trans & Dist

View full text Add to dashboard Cite

Daily peak load forecasting is crucial for the operation of bulk power systems, including economic dispatch and unit commitment. It is also essential for peak load shaving and load management in distribution systems. In power markets, peak load forecasting helps participants develop bidding strategies. This paper proposes a new method, using a hybrid convolutional neural network (CNN) that is cascaded with a fully-connected network, for making week-ahead daily peak load forecasts. The proposed method uses three loops to obtain the optimal CNN: The outer loop performs crossover/mutation operations and tournament selection to produce chromosomes to optimize the network topology and hyperparameters (such as kernel size) of the hybrid CNN by genetic algorithms; the middle loop deals with the order of chromosomes; the inner loop optimizes the synaptic weights and parameters (e.g. values of a kernel) using Adam optimizer. Daily peak load data and corresponding meteorological data for Taiwan are explored. Simulation results show that the proposed method outperforms the traditional CNN, multi-layer neural network, recurrent neural network, support vector regression and vector autoregressive moving average model.

show abstract

Peak Load Forecasting Based on Long Short Term Memory

Cited by 5 publications

References 15 publications

Electrical peak demand forecasting- A review

Electrical peak demand forecasting- A review

Time Series Forecasting for Average Temperature in 96041 Station Using Long Short-Term Memory Model

Week‐ahead daily peak load forecasting using genetic algorithm‐based hybrid convolutional neural network

Contact Info

Product

Resources

About