Short Term Load Forecasting Using a Neural Network Based Time Series Approach

Dwijayanti, Suci; Hagan, Martin T.

doi:10.1109/aims.2013.11

Cited by 14 publications

(7 citation statements)

References 34 publications

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“…Recently, a number of studies have gone beyond traditional statistical forecasting schemes (e.g. as in [3], [4]) and adopt some of the benefits offered by machine learning (ML)oriented techniques for STLF. Mainly, the direction towards ML solutions is seen as beneficial due to the ability of such approaches to model underlying patterns among several linear and non-linear data features gathered by heterogeneous inputs.…”

Section: Background and Related Workmentioning

confidence: 99%

Appliance-level Short-Term Load Forecasting using Deep Neural Networks

Din

Mauthe

Marnerides

2018

2018 International Conference on Computing, Networking and Communications (ICNC)

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The recently employed demand-response (DR) model enabled by the transformation of the traditional power grid to the SmartGrid (SG) allows energy providers to have a clearer understanding of the energy utilisation of each individual household within their administrative domain. Nonetheless, the rapid growth of IoT-based domestic appliances within each household in conjunction with the varying and hard-to-predict customer-specific energy requirements is regarded as a challenge with respect to accurately profiling and forecasting the day-today or week-to-week appliance-level power consumption demand. Such a forecast is considered essential in order to compose a granular and accurate aggregate-level power consumption forecast for a given household, identify faulty appliances, and assess potential security and resilience issues both from an enduser as well as from an energy provider perspective. Therefore, in this paper we investigate techniques that enable this and propose the applicability of Deep Neural Networks (DNNs) for short-term appliance-level power profiling and forecasting. We demonstrate their superiority over the past heavily used Support Vector Machines (SVMs) in terms of prediction accuracy and computational performance with experiments conducted over real appliance-level dataset gathered in four residential households.

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Section: Background and Related Workmentioning

confidence: 99%

Appliance-level Short-Term Load Forecasting using Deep Neural Networks

Din

Mauthe

Marnerides

2018

2018 International Conference on Computing, Networking and Communications (ICNC)

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“…Additionally, for testing accuracy rate of model mean absolute percentage error (MAPE) is utilized. MAPE is the better measure performance evaluation [13]. There are various factors which affects load forecasting i.e.…”

Section: Table-1 Various Hybrid Forecasting Approaches Forecasting Momentioning

confidence: 99%

Electric Forecasting using Nature Inspired Optimization Techniques

Singh*,

Srivastava,

Singh

2019

IJEAT

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Now a day, there exists huge competition among power industries in terms of fulfilling various customers' electrical needs. Reliable and quality power supply is no doubt a basic need for all power consumers. Moreover, planning & operation engineers also targets for proper unit commitment, economic power dispatch, etc., and highly depend upon good power system planning. Therefore, Electric Forecasting (EF) is a major criterion for power engineers. In this manuscript, Artificial Neural Network (ANN), being a well established tool for modeling non-linear and black box systems, is used to forecast hydro generation power plant, energy met and peak demand of India.Furthermore, in this competitive world, ANN model is further optimized using genetic algorithm (GA) and particle swarm optimization (PSO) to explore accurate forecasting model with minimal amount of error. These optimization methods explore highly diversified search area, resulting in more accurate forecasting results in comparison to ANN when trained with standard back propagation training algorithm. India. His research area includes power system planning, transformer, energy efficiency, load forecasting, neural network. Navneet Kumar Singh received the PhD degree from Motilal Nehru National Institute of Technology Allahabad, Prayagraj, India. Currently he is with Electrical Engineering Department, MNNIT Allahabad, India. His research area includes load forecasting, power system planning and Application of Artificial Intelligence (AI) techniques in power system.

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“…Other comparative studies, considering different ANNs, have been presented in the specialized literature with this emphasis, e.g., in [34][35][36][37]. The results from the comparative studies show that the ANN model is a superior method for load forecasting, owing to its ability to handle load data and its lower MAPE.…”

Section: Wherementioning

confidence: 99%

Multinodal load forecasting for distribution systems using a fuzzy-artmap neural network

Abreu

Amorim

Santos-Junior

et al. 2018

Applied Soft Computing

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This work proposes a predictor system (multinodal forecasting) considering several points of an electrical network, such as substations, transformers, and feeders, based on an adaptive resonance theory (ART) neural network family. It is a problem similar to global forecasting, with the main difference being the strategy to align the input and output of the data with several parallel neural modules. Considering that multinodal prediction is more complex compared to global prediction, the multinodal prediction will use a fuzzy-ARTMAP neural network and a global load participation factor. The advantages of this approach are as follows: (1) the processing time is equivalent to the processing required for global forecasting (i.e., the additional time processing is quite low); and (2) Fuzzy-ARTMAP neural networks converge significantly faster than backpropagation neural networks (improved benchmark in precision). The preference for neural networks of the ART family is due to the characteristic stability and plasticity that these architectures have to provide results in a fast and precise way. To test the proposed forecast system, the results are presented for nine substations from the database of an electrical company.

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Short Term Load Forecasting Using a Neural Network Based Time Series Approach

Cited by 14 publications

References 34 publications

Appliance-level Short-Term Load Forecasting using Deep Neural Networks

Appliance-level Short-Term Load Forecasting using Deep Neural Networks

Electric Forecasting using Nature Inspired Optimization Techniques

Multinodal load forecasting for distribution systems using a fuzzy-artmap neural network

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