Short-Term Power Load Forecasting with Deep Belief Network and Copula Models

He, Yusen; Deng, Jiahao; Li, Huajin

doi:10.1109/ihmsc.2017.50

Cited by 52 publications

(20 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The result describes that it shows a feasible prediction. Similarly, the experimental analysis using DBN 52 to predict hourly forecast in an urban area in Texas, USA, based on the dataset for a year shows accurate prediction over support vector regression (SVR), classical ANN, and extreme learning machine (ELM). DNN approach of demand prediction day ahead is proposed 53 with a dataset of 90 days in the Iberian area made to train multi-layered NN with a combination of multiple active functions.…”

Section: Demand Forecastingmentioning

confidence: 93%

Deep learning and reinforcement learning approach on microgrid

Kumar¹,

Kandasamy

Ramanathan

2020

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

Microgrid is a new era in the power system and it has more scope of investigation on research. Due to an increase in demand and future expansion of the power system, analyzing the complexities of the network becomes a challenging task. Artificial intelligence plays a vital role in resolving such issues in a microgrid in various aspects. Owing to the rapid growth of periodical update in computational cost reduction, enhanced data analysis-based algorithm artificial intelligence enters into new epoch Artificial Intelligence AI 2.0. Based on such approach, machine learning has been evolved as AI 2.0 initially. Now, it develops branches like deep learning, reinforcement learning, and a combination of both deep reinforcement learning algorithms. These algorithms are precise to attain higher priority in decision-making under a complex network. This paper deals with numerous challenges of the above algorithm to state the significance of AI 2.0 and summarization of their application toward microgrid is useful to analyze the power system. K E Y W O R D S deep learning, deep reinforcement learning, microgrid, reinforcement learning 1 | INTRODUCTION Microgrid is a cluster of distributed generators (DG), stored energy system, local loads along with protecting and monitoring devices. Microgrid has its own specialty in operating under two modes of operation: autonomous and grid-connected mode, mode selection attained by the use of static transfer switch that bridges operation selection in microgrid. The presence of multiple DGs creates a distribution access problem in multipoint is transformed into a concentrated

show abstract

Section: Demand Forecastingmentioning

confidence: 93%

Deep learning and reinforcement learning approach on microgrid

Kumar¹,

Kandasamy

Ramanathan

2020

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

show abstract

“…This DNN model is later used to produce a day-ahead forecast of 24 hours load profile from the past data observations. He et al [24] proposed a model to forecast the hourly load of a power grid. Their model combines the comovement analysis from Copula model with layer-wise pretraining-based deep belief network.…”

Section: ) Deep Learning (Dl)mentioning

confidence: 99%

A Comprehensive Review of the Load Forecasting Techniques Using Single and Hybrid Predictive Models

et al. 2020

View full text Add to dashboard Cite

Load forecasting is a pivotal part of the power utility companies. To provide load-shedding free and uninterrupted power to the consumer, decision-makers in the utility sector must forecast the future demand for electricity with a minimum amount of error percentage. Load prediction with less percentage of error can save millions of dollars to the utility companies. There are numerous Machine Learning (ML) techniques to amicably forecast the demand of electricity among which the hybrid models show the best result. Two or more than two predictive models are amalgamated to design a hybrid model, each of which provides improved performances by the merit of individual algorithms. This paper reviews the current stateof-the-art of electric load forecasting technologies and presents recent works pertaining to the combination of different ML algorithms into two or more methods for the construction of hybrid models. A comprehensive study of each single and multiple load forecasting model is performed with an in-depth analysis of their advantages, disadvantages, and functions. A comparison between their performance in terms of Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) values are developed with pertinent literature of several models to aid the researchers with the selection of suitable models for load prediction.

show abstract

“…The authors of [23][24][25] used the DBN to forecast power load, the DBN is adopted in this study to predict the dynamic frequency deviation, rotor speed recovery time, the active power sags of LWTG and the minimum rotor speed under different wind speeds with different de-loading ratios and control parameters. After that, the optimal de-loading ratios and control parameters for LWTG can be found for the best performance of frequency regulation.…”

Section: Control Parameter Optimisation Based On Dbnmentioning

confidence: 99%