Long-Term Electricity Load Forecasting Considering Volatility Using Multiplicative Error Model

Khuntia, Swasti R.; Rueda, José L.; Meijden, Mart A. M. M. van der

doi:10.3390/en11123308

Cited by 24 publications

(10 citation statements)

References 55 publications

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“…Khuntia et al propose in [21] an approach for the long-term prediction of the electricity load by means of a "multiplicative error model (MEM)" taking into account the volatility. The electricity load data has been retrieved from a regional dispatch power operator from the United States, while the recession data has been gathered from the "National Bureau of Economic Research".…”

Section: Literature Reviewmentioning

confidence: 99%

Designing, Developing and Validating a Forecasting Method for the Month Ahead Hourly Electricity Consumption in the Case of Medium Industrial Consumers

Petroşanu¹

2019

Processes

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An accurate forecast of the electricity consumption is particularly important to both consumers and system operators. The purpose of this study is to develop a forecasting method that provides such an accurate forecast of the month-ahead hourly electricity consumption in the case of medium industrial consumers, therefore assuring an intelligent energy management and an efficient economic scheduling of their resources, having the possibility to negotiate in advance appropriate billing tariffs relying on accurate hourly forecasts, in the same time facilitating an optimal energy management for the dispatch operator. The forecasting method consists of developing first non-linear autoregressive, with exogenous inputs (NARX) artificial neural networks (ANNs) in order to forecast an initial daily electricity consumption, a forecast that is being further processed with custom developed long short-term memory (LSTM) neural networks with exogenous variables support in order to refine the daily forecast as to achieve an accurate hourly forecasted consumed electricity for the whole month-ahead. The obtained experimental results (highlighted also through a very good value of 0.0244 for the root mean square error performance metric, obtained when forecasting the month-ahead hourly electricity consumption and comparing it with the real consumption), the validation of the developed forecasting method, the comparison of the method with other forecasting approaches from the scientific literature substantiate the fact that the proposed approach manages to fill a gap in the current body of knowledge consisting of the need of a high-accuracy forecasting method for the month-ahead hourly electricity consumption in the case of medium industrial consumers. The developed forecasting method targets medium industrial consumers, but, due to its accuracy, it can also be a useful tool for promoting innovative business models with regard to industrial consumers willing to produce a part of their own electricity using renewable energy resources, benefiting from reduced production costs and reliable electricity prices.

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Section: Literature Reviewmentioning

confidence: 99%

Designing, Developing and Validating a Forecasting Method for the Month Ahead Hourly Electricity Consumption in the Case of Medium Industrial Consumers

Petroşanu¹

2019

Processes

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“…The uncertainty and nonlinearity of the electricity load are gradually increasing, and the relationship between the demand of the source and the load-side consumption is maintained, which is consistently increasing the accuracy of load prediction. Improving the accuracy of user-side load prediction plays an important role in power grid power planning and power scheduling [20].…”

Section: Motivation and Problem Statementmentioning

confidence: 99%

Research on Short-Term Load Prediction Based on Seq2seq Model

Gong

Mahato

et al. 2019

Energies

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Electricity load prediction is the primary basis on which power-related departments to make logical and effective generation plans and scientific scheduling plans for the most effective power utilization. The perpetual evolution of deep learning has recommended advanced and innovative concepts for short-term load prediction. Taking into consideration the time and nonlinear characteristics of power system load data and further considering the impact of historical and future information on the current state, this paper proposes a Seq2seq short-term load prediction model based on a long short-term memory network (LSTM). Firstly, the periodic fluctuation characteristics of users’ load data are analyzed, establishing a correlation of the load data so as to determine the model’s order in the time series. Secondly, the specifications of the Seq2seq model are given preference and a coalescence of the Residual mechanism (Residual) and the two Attention mechanisms (Attention) is developed. Then, comparing the predictive performance of the model under different types of Attention mechanism, this paper finally adopts the Seq2seq short-term load prediction model of Residual LSTM and the Bahdanau Attention mechanism. Eventually, the prediction model obtains better results when merging the actual power system load data of a certain place. In order to validate the developed model, the Seq2seq was compared with recurrent neural network (RNN), LSTM, and gated recurrent unit (GRU) algorithms. Last but not least, the performance indices were calculated. when training and testing the model with power system load data, it was noted that the root mean square error (RMSE) of Seq2seq was decreased by 6.61%, 16.95%, and 7.80% compared with RNN, LSTM, and GRU, respectively. In addition, a supplementary case study was carried out using data for a small power system considering different weather conditions and user behaviors in order to confirm the applicability and stability of the proposed model. The Seq2seq model for short-term load prediction can be reported to demonstrate superiority in all areas, exhibiting better prediction and stable performance.

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“…In the short-term, the focus is on predicting energy consumption values, to aid in planning for energy production and grid maintenance. Whereas in the longterm, planning for grid expansions and future investments are the objective, even influencing the economic development of a given geographic zone [2].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Short-term Instant Energy Consumption Forecasting in the Manufacturing Sector

Oliveira¹,

Sousa²,

Praça³

2022

Preprint

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Electricity is a volatile power source that requires great planning and resource management for both short and long term. More specifically, in the short-term, accurate instant energy consumption forecasting contributes greatly to improve the efficiency of buildings, opening new avenues for the adoption of renewable energy. In that regard, data-driven approaches, namely the ones based on machine learning, are begin to be preferred over more traditional ones since they provide not only more simplified ways of deployment but also state of the art results. In that sense, this work applies and compares the performance of several deep learning algorithms, LSTM, CNN, mixed CNN-LSTM and TCN, in a real testbed within the manufacturing sector. The experimental results suggest that the TCN is the most reliable method for predicting instant energy consumption in the short-term.

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Long-Term Electricity Load Forecasting Considering Volatility Using Multiplicative Error Model

Cited by 24 publications

References 55 publications

Designing, Developing and Validating a Forecasting Method for the Month Ahead Hourly Electricity Consumption in the Case of Medium Industrial Consumers

Designing, Developing and Validating a Forecasting Method for the Month Ahead Hourly Electricity Consumption in the Case of Medium Industrial Consumers

Research on Short-Term Load Prediction Based on Seq2seq Model

Deep Learning for Short-term Instant Energy Consumption Forecasting in the Manufacturing Sector

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