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

Abstract

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.

Electricity Consumption Forecasting Based on a Bidirectional Long-Short-Term Memory Artificial Neural Network

“…Another previous work of one of the authors of the current paper targeted the forecasting of the consumed electricity for a whole month on an hourly resolution, in the case of an industrial consumer [31]. Even if the profiles of the two electricity consumers differ, we investigated the approach consisting of using the ANNs based on the NARX model for obtaining first a forecast of the total daily aggregated electricity consumption that is being processed further using LSTM ANNs for refining the forecasted aggregated daily electricity consumption for a whole month on an hourly resolution.…”

“…However, even if when we devised our forecasting approach, we remarked that in this particular case, the two datasets given by the contractor did not contain such type of values, we designed this step to make sure that the devised forecasting method can be applied for a wider range of commercial center-type consumer cases, similar to the one for which we conducted the current study and therefore to be able to generalize the devised forecasting approach. In order to solve the issues regarding the potential missing or abnormal values, we designed this step to apply a gap-filling approach that has proven its effectiveness when applied in previous studies, some of them being conducted within our research team [30][31][32]34], the method being based on a linear interpolation approach.…”

Toward Better PV Panel’s Output Power Prediction; a Module Based on Nonlinear Autoregressive Neural Network with Exogenous Inputs

“…NARX is a neural network with recurrent dynamic behavior [38][39][40] that has been effectively used for time series problems estimation. The main difference between MLP and NARX is that NARX allows a weighted feedback connection between layers of neurons.…”

“…The NARX neural network can be constructed into one of two architectures [31], the parallel architecture (namely close-loop architecture), and the series-parallel architecture (namely, open-loop architecture), see Figure 3. NARX is a neural network with recurrent dynamic behavior [38][39][40] that has been effectively used for time series problems estimation. The main difference between MLP and NARX is that NARX allows a weighted feedback connection between layers of neurons.…”

Load State Identification Method for Ball Mills Based on Improved EWT, Multiscale Fuzzy Entropy and AEPSO_PNN Classification

“…As a tool for recognition and classification, an artificial neural network is a model abstracted based on neural network theory that originates from the field of physiology. Such models can be used for arbitrary data clustering and pattern classification and are widely used for tasks such as pattern recognition [27][28][29]. Specifically, a probabilistic neural network (PNN) is an artificial neural network with the advantages of a fast training speed, simple parameter adjustment, and good classification performance [30].…”