Abstract
The accurate forecasting of the hourly month-ahead electricity consumption represents a very important aspect for non-household electricity consumers and system operators, and at the same time represents a key factor in what regards energy efficiency and achieving sustainable economic, business, and management operations. In this context, we have devised, developed, and validated within the paper an hourly month ahead electricity consumption forecasting method. This method is based on a bidirectional long-short-term memory (BiLSTM) artificial neural network (ANN) enhanced with a multiple simultaneously decreasing delays approach coupled with function fitting neural networks (FITNETs). The developed method targets the hourly month-ahead total electricity consumption at the level of a commercial center-type consumer and for the hourly month ahead consumption of its refrigerator storage room. The developed approach offers excellent forecasting results, highlighted by the validation stage’s results along with the registered performance metrics, namely 0.0495 for the root mean square error (RMSE) performance metric for the total hourly month-ahead electricity consumption and 0.0284 for the refrigerator storage room. We aimed for and managed to attain an hourly month-ahead consumed electricity prediction without experiencing a significant drop in the forecasting accuracy that usually tends to occur after the first two weeks, therefore achieving a reliable method that satisfies the contractor’s needs, being able to enhance his/her activity from the economic, business, and management perspectives. Even if the devised, developed, and validated forecasting solution for the hourly consumption targets a commercial center-type consumer, based on its accuracy, this solution can also represent a useful tool for other non-household electricity consumers due to its generalization capability.
Highlights
As in one of our research group’s previous studies [30], the profile of the electricity consumer was of commercial center-type and the forecasting horizon consisted of hourly month-ahead predictions, we first tried to investigate if the employed methods from that study, namely an artificial neural network (ANN) approach based on the non-linear autoregressive (NAR) and the non-linear autoregressive with exogenous inputs (NARX) models could provide satisfactory results for our current situation
Analyzing the developed bidirectional long-short-term memory (BiLSTM) ANNs, we remarked that regarding the number of hidden units, starting with n = 300, and considering the dimension of the minibatchsize as 128, the performance reached a plateau so that any further increase in the number of neurons no longer brought a noticeable increase in the performance accuracy, but only a significant increase in the execution time (e.g., 2713.504 s = 45.23 min when considering the adaptive moment estimation (ADAM) training algorithm with n = 1000 and d = 1, compared to 1259.877 s = 20.99 min registered in the case when considering the ADAM training algorithm with n = 300 and d = 1)
For obtaining a proper evaluaof the forecasting accuracy, we evaluated the and we remarked that the registered tion of the forecasting accuracy, we evaluated the root mean square error (RMSE) and we remarked that the regisvalues were very good, namely: 0.0307 during the training process, 0.0327 for the integral tered values were very good, namely: 0.0307 during the training process, 0.0327 for the set, and 0.0495 during the testing process
Summary
In order to attain worldwide energy efficiency, a factor of major importance consists in providing an accurate prediction of the consumers’ electricity consumption belonging to the large non-household category Such an accurate forecast helps in optimizing the electricity consumption and influencing the consumption strategy, and facilitates the efforts made in view of attaining an appropriate environment and natural resource management. In this context, the accurate forecasting of the hourly month ahead electricity consumption is a very important aspect for non-household electricity consumers and system operators, and at the same time represents an essential factor in what regards energy efficiency
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