In the current trend of consumption, electricity consumption will become a very high cost for the end-users. Consumers acquire energy from suppliers who use short, medium, and long-term forecasts to place bids in the power market. This study offers a detailed analysis of relevant literature and proposes a deep learning methodology for forecasting industrial electric usage for the next 24 h. The hourly load curves forecasted are from a large furniture factory. The hourly data for one year is split into training (80%) and testing (20%). The algorithms use the previous two weeks of hourly consumption and exogenous variables as input in the deep neural networks. The best results prove that deep recurrent neural networks can retain long-term dependencies in high volatility time series. Gated recurrent units (GRU) obtained the lowest mean absolute percentage error of $4.82\%$ for the testing period. The GRU improves the forecast by $6.23\%$ compared to the second-best algorithm implemented, a combination of GRU and Long short-term memory (LSTM). From a practical perspective, deep learning methods can automate the forecasting processes and optimize the operation of power systems.

Integration of industrial consumers into the smart grid concept can be facilitated by optimizing load forecasting for industrial consumers. Minimizing forecast errors can improve the supplier-consumer relationship by reducing balancing costs and anticipate possible network faults. The present paper aims to research the efficiency of machine learning applied for industrial load. The dataset consists of hourly recorded values for electricity consumption generated by a meat processing facility. In the context of installing complex monitoring systems with high frequency recording intervals, huge amounts of data will be generated that require detalied analysis and real time processing, otherwise the investments in the smart grid are not justified, consequently disfavouring the development and digitization of electrical networks. Integration of the industrial consumer into the smart grid concept can be applied in great detail at large industrial consumers through robust forecasting. Forecasting the energy behaviour of a industrial consumer is a difficult task, high forecasting errors have been obtained due to the unpredictability of the consumer.

Short-term load forecasting predetermines how power systems operate because electricity production needs to sustain demand at all times and costs. Most load forecasts for the non-residential consumers are empirically done either by a customer’s employee or supplier personnel based on experience and historical data, which is frequently not consistent. Our objective is to develop viable and market-oriented machine learning models for short-term forecasting for non-residential consumers. Multiple algorithms were implemented and compared to identify the best model for a cluster of industrial and commercial consumers. The article concludes that the sliding window approach for supervised learning with recurrent neural networks can learn short and long-term dependencies in time series. The best method implemented for the 24 h forecast is a Gated Recurrent Unit (GRU) applied for aggregated loads over three months of testing data resulted in 5.28% MAPE and minimized the cost with 5326.17 € compared with the second-best method LSTM. We propose a new model to evaluate the gap between evaluation metrics and the financial impact of forecast errors in the power market environment. The model simulates bidding on the power market based on the 24 h forecast and using the Romanian day-ahead market and balancing prices through the testing dataset.

Reliable real-time power measurement data represents a vital role in understanding energy behavior from a consumer standpoint. Energy efficiency is directly influenced by monitoring electric parameters. With collected live data, the challenge becomes how to organize and analyze datasets of information. The purpose is to design and build a low-cost electronic monitoring device using Arduino and IoT for electricity monitoring. The main capabilities are to measure, record, and transmit using wi-fi connection all the essential electric parameters in a web-based application. Monitored electric parameters are voltage, current, power factor, phase angle, power, and energy. The developed prototype is versatile in multiple types of low voltage installations, mainly household appliances. Measurements are transmitted remotely in an online database, where the data can be stored and analyzed in real-time. The solution has permanent storage and facilitates consultation of past and current records. The market approach is possible because of the low-cost of the device. A commercial version would be suitable and could easily integrate Smart Grid applications. Using real-time energy data and monitoring software, consumers can measure high consumption loads, reduce base loads, evaluate peak and off-peak hours and identify irregularities. optimize usage scheduling and track energy-saving targets and goals more accurately.

Short-term electricity load forecasting has attracted considerable attention as a result of the crucial role that it plays in power systems and electricity markets. This paper presents a novel hybrid forecasting method that combines an autoregressive model with Gaussian process regression. Mixed-user, hourly, historical data are used to train, validate, and evaluate the model. The empirical wavelet transform was used to preprocess the data. Among the perturbing factors, the most influential predictors that were recorded were the weather factors and day type. The developed methodology is upgraded using a novel closed-loop algorithm that uses the forecasting values and influential factors to predict the residuals. Most performance indicators that are computed indicate that forecasting the residuals actually improves the method’s precision, decreasing the mean absolute percentage error from 5.04% to 4.28%. Measured data are used to validate the effectiveness of the presented approach, making it a suitable tool for use in load forecasting by utility companies.

Integration of industrial consumers into the smart grid concept can be facilitated by optimizing short and very short-term forecasts of load curves for industrial consumers. Minimizing forecast errors can improve the supplier-consumer relationship by reducing supplier balancing costs, which can lower the energy bill, and anticipate possible network faults or network congestion which means improved operation across the entire power system. The present paper aims at demonstrating the usefulness of energy forecasting by using machine learning methods. In the context of installing monitoring systems with load curve recording data at short intervals of time, huge amounts of data are obtained that need to be capitalized in real time (automated), otherwise the investments necessary to modernize the way in which the energy is used are not justified, consequently disfavouring the development and digitization of electrical networks. Integration of the industrial consumer into the smart grid concept can be applied in detail at large industrial consumers where forecasts can be made on certain areas inside factories or electric equipment with high installed electric capacity. The use of smart grid technologies removes the current limits for monitoring, processing and communication between consumption and production. To be able to capitalize on the forecasts, a very important aspect is the automation of their realization and quick access to the decision makers who may be human or other smart grid integrated systems. That is why the present paper proposes the use of machine learning methods in the short and very short forecast, and the results will be compared with the classical forecasting methods. Hourly load curves from the industrial consumers will be used for forecasts.