Comparative Analysis of Load Forecasting Models for Varying Time Horizons and Load Aggregation Levels

Burg, Leonard; Gürses-Tran, Gonca; Madlener, Reinhard; Monti, Antonello

doi:10.3390/en14217128

Cited by 15 publications

(3 citation statements)

References 28 publications

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“…Electrical consumption is often influenced by different climatic factors, such as temperature and humidity [2]. Therefore, the short-term prediction models often include meteorological and temporal parameters [5], [39], temperature and wind speed [40], humidity and total precipitable liquid water [41]. Additionally, there is a distinction between working days and weekends or holidays because they show different electrical load-consuming profiles.…”

Section: Methodsmentioning

confidence: 99%

Electricity Consumption Prediction in an Electronic System Using Artificial Neural Networks

2022

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The tremendous rise of electrical energy demand worldwide has led to many problems related to efficient use of electrical energy, consequently posing difficult challenges to electricity consumers of all levels—from households to large companies’ facilities. Most of these challenges could be overcome by the accurate prediction of electricity demand. Additionally, balance responsibility includes the penalty-based financial mechanism causing extra expense for badly estimated consumption, above the allowed imbalance limits. In this paper, a method for electricity consumption prediction based on artificial neural networks is proposed. The electricity consumption dataset is obtained from a cold storage facility, which generates data in hourly intervals. The data obtained are measured for a period of over 2 years and then separated to four seasons, so different models are developed for each season. Five different network structures (ordinary RNN, LSTM, GRU, bidirectional LSTM, bidirectional GRU) for five different values of horizon, i.e., input data (one day, two days, four days, one week, two weeks) are examined. Performance indices, such as mean absolute percentage error (MAPE), root mean square error (RMSE), mean absolute error (MAE) and mean square error (MSE), are used in order to obtain qualitative and quantitative comparisons among the obtained models. The results show that the modifications of recurrent neural networks perform much better than ordinary recurrent neural networks. GRU and LSTMB structures with horizons of 168h and 336h are found to have the best performances.

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Section: Methodsmentioning

confidence: 99%

Electricity Consumption Prediction in an Electronic System Using Artificial Neural Networks

2022

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“…Digital twins of energy assets 13 [9][10][11][12][13][14][15][16][17][18][19][20][21] Energy forecasting 14 [1,[22][23][24][25][26][27][28][29][30][31][32][33][34] Optimization and coordination 18 [35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51][52] VPP applications in smart grids Energy services delivery 31 [4,5,22,35,38, Local energy autonomy 21 [5,…”

Section: Vpp Concepts and Technologymentioning

confidence: 99%

“…Analyzing the current state of the art here is very difficult in comparing existing models and technologies in terms of performance, prediction accuracy, and impact on the virtual aggregation of energy assets [32]. The quality of the prediction outcome is influenced by factors such as seasonality, social factors, the intrinsic physical parameters of the assets, time step, and prediction interval [23].…”

Section: Energy Forecastingmentioning

confidence: 99%

Virtual Power Plant Optimization in Smart Grids: A Narrative Review

2022

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Virtual power plants (VPPs) are promising solutions to address the decarbonization and energy efficiency goals in the smart energy grid. They assume the coordination of local energy resources such as energy generation, storage, and consumption. They are used to tackle problems brought by the stochastic nature of renewable energy, lack of energy storage devices, or insufficient local energy flexibility on the demand side. VPP modeling, management, and optimization are open to research problems that should consider, on one side, the local constraints in the operation of the energy resources and power flows and the energy grid’s sustainability objectives on the other side. There are multiple goals to create a VPP, such as to deliver energy services on a market or to the grid operator, to operate a microgrid in autonomy decoupled from the main grid, or to sustain local energy communities. In this paper, we present the results of a narrative review carried out on the domain of VPP optimization for the local energy grid integration. We have defined a search strategy that considers highly rated international databases (i.e., Elsevier, IEEE, and MDPI) in a six-year timeframe and applied objective inclusion/exclusion criteria for selecting articles and publications for the review; 95 articles have been analyzed and classified according to their objectives and solutions proposed for optimizing VPP integration in smart grids. The results of the study show that VPP concepts and applications are well addressed in the research literature, however, there is still work to be done on: engaging prosumers and citizens in such a virtual organization, developing heuristics to consider a wider range of local and global constraints and non-energy vectors, and to decentralize and make transparent the services delivery and financial settlement towards community members. This study can help researchers to understand the current directions for VPP integration in smart grids. As a next step we plan to further analyze the open research directions related to this problem and target the development of innovative solutions to allow the integration of multi-energy assets and management of cross energy sector services in energy communities.

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Deep learning-based load forecasting considering data reshaping using MATLAB\Simulink

Hamad

Abdulrahman

2022

Int J Energy Environ Eng

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Load forecasting is a nonlinear problem and complex task that plays a key role in power system planning, operation, and control. A recent study proposed a deep learning approach called historical data augmentation (HDA) to improve the accuracy of the load forecasting model by dividing the input data into several yearly sub-datasets. When the original data is associated with high time step changes from 1 year to another, the approach was not found as effective as it should be for long-term forecasting because the time-series information is disconnected by the approach between the end of 1-year sub-data and the beginning of the next-year sub-data. Alternatively, this paper proposes the use of 2-year sub-dataset in order to connect the two ends of the yearly subsets. A correlation analysis is conducted to show how the yearly datasets are correlated to each other. In addition, a Simulink-based program is introduced to simulate the problem which has an advantage of visualizing the algorithm. To increase the model generalization, several inputs are considered in the model including load demand profile, weather information, and some important categorical data such as week-day and weekend data that are embedded using onehot encoding technique. The deep learning methods used in this study are the long short-term memory (LSTM) and gated rest unit (GRU) neural networks which have been increasingly employed in the recent years for time series and sequence problems. To provide a theoretical background on these models, a new picturized detail is presented. The proposed method is applied to the Kurdistan regional load demands and compared with classical methods of data inputting demonstrating improvements in both the model accuracy and training time.

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Comparative Analysis of Load Forecasting Models for Varying Time Horizons and Load Aggregation Levels

Cited by 15 publications

References 28 publications

Electricity Consumption Prediction in an Electronic System Using Artificial Neural Networks

Electricity Consumption Prediction in an Electronic System Using Artificial Neural Networks

Virtual Power Plant Optimization in Smart Grids: A Narrative Review

Deep learning-based load forecasting considering data reshaping using MATLAB\Simulink

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