Abstract
Background: With the development of smart grids, accurate electric load forecasting has become increasingly important as it can help power companies in better load scheduling and reduce excessive electricity production. However, developing and selecting accurate time series models is a challenging task as this requires training several different models for selecting the best amongst them along with substantial feature engineering to derive informative features and finding optimal time lags, a commonly used input features for time series models. Methods: Our approach uses machine learning and a long short-term memory (LSTM)-based neural network with various configurations to construct forecasting models for short to medium term aggregate load forecasting. The research solves above mentioned problems by training several linear and non-linear machine learning algorithms and picking the best as baseline, choosing best features using wrapper and embedded feature selection methods and finally using genetic algorithm (GA) to find optimal time lags and number of layers for LSTM model predictive performance optimization. Results: Using France metropolitan’s electricity consumption data as a case study, obtained results show that LSTM based model has shown high accuracy then machine learning model that is optimized with hyperparameter tuning. Using the best features, optimal lags, layers and training various LSTM configurations further improved forecasting accuracy. Conclusions: A LSTM model using only optimally selected time lagged features captured all the characteristics of complex time series and showed decreased Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) for medium to long range forecasting for a wider metropolitan area.
Highlights
Load forecasting enables utility providers to model and forecast power loads to preserve a balance between production and demand, to reduce production cost, to estimate realistic energy prices and to manage scheduling and future capacity planning
We propose a long short-term memory (LSTM)-Recurrent Neural Networks (RNN)-based model for aggregated demand side load forecast over short- and medium-term monthly horizons
We present an overview of the methodology, methodology, and we describe for each methodology components its detailed mission followed and thenbywe describe for each methodology components its detailed mission followed by an illustration an illustration on the Réseau de Transport dÉlectricité (RTE) power consumption data set, our case on the Réseau study. de Transport d'Électricité (RTE) power consumption data set, our case study
Summary
Load forecasting enables utility providers to model and forecast power loads to preserve a balance between production and demand, to reduce production cost, to estimate realistic energy prices and to manage scheduling and future capacity planning. The literature reveals that short-term demand forecasting has attracted substantial attention. Such forecasting is important for power system control, unit commitment, economic dispatch, and electricity markets. Methods: Our approach uses machine learning and a long short-term memory (LSTM)-based neural network with various configurations to construct forecasting models for short to medium term aggregate load forecasting. Conclusions: A LSTM model using only optimally selected time lagged features captured all the characteristics of complex time series and showed decreased Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) for medium to long range forecasting for a wider metropolitan area. Backgrounds on LSTM‐RNN, on benchmark machine learning and on the evaluation models andforecasting on the forecasting evaluation metrics. RNN is is aa special special type type of of ANN use of information due due to to directed directed
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