The smart grid concept is introduced to accelerate the operational efficiency and enhance the reliability and sustainability of power supply by operating in self-control mode to find and resolve the problems developed in time. In smart grid, the use of digital technology facilitates the grid with an enhanced data transportation facility using smart sensors known as smart meters. Using these smart meters, various operational functionalities of smart grid can be enhanced, such as generation scheduling, real-time pricing, load management, power quality enhancement, security analysis and enhancement of the system, fault prediction, frequency and voltage monitoring, load forecasting, etc. From the bulk data generated in a smart grid architecture, precise load can be predicted before time to support the energy market. This supports the grid operation to maintain the balance between demand and generation, thus preventing system imbalance and power outages. This study presents a detailed review on load forecasting category, calculation of performance indicators, the data analyzing process for load forecasting, load forecasting using conventional meter information, and the technology used to conduct the task and its challenges. Next, the importance of smart meter-based load forecasting is discussed along with the available approaches. Additionally, the merits of load forecasting conducted using a smart meter over a conventional meter are articulated in this paper.

Load forecasting (LF) plays a crucial role in energy production planning and scheduling, simplifying budgeting processes, and improving power supply reliability. The available integrated solutions are superior to conventional approaches while considering the uncertainties of weather conditions. The primary objective of LF is to establish an optimal load model for the power grid, conducted offline, to achieve accurate predictions, thereby minimizing operational costs and enhancing grid stability. In this work, an integrated LF model is proposed that uses modified combined ensemble empirical mode decomposition with adaptive noise (MCEEMDAN), Shannon entropy (SE), and long short-term memory (LSTM) techniques. To demonstrate the efficacy of the proposed method, this manuscript utilizes a real-time dataset containing actual load data, social & temporal variables and meteorological parameters including temperature, humidity, and rainfall, gathered from Raipur region in Chhattisgarh state, India. A comparative analysis of the proposed method is conducted against other available approaches, including various time-series decomposition methods, different machine learning techniques, and alternative test system.

To reduce the carbon footprint and decentralize the future power market, bulk renewable energy sources (RESs) are penetrating the electric grid. Although RESs are a great solution to meet the ever-increasing load demand, their dependency on the meteorological conditions is a challenge particularly when thinking about the complete replacement of conventional power generation. To balance the demand and supply, accurate forecasting of load demand and generation from RESs is essential for the suppliers to ensure efficient energy management and precise scheduling. An Adaptive Chirp Mode Decomposition (ACMD) and Attention-Based Bidirectional Long Short-Term Memory (At-BiLSTM)-based short-term forecasting method is proposed in this paper to predict the load demand, and total power generation from RESs. This hybrid approach effectively addresses the uncertainties in the load demand and the RES generation by capturing correlations between the decomposed modes, original power information, and weather variables. For accurate generation forecasting, meteorological data is used separately for both the wind and solar systems. To evaluate the accuracy of the model, performance indicators are used in terms of errors. The proposed method is compared with various existing standalone and hybrid methods to assess the efficacy of the method. The standalone methods include BPNN, RNN, GRU, LSTM, Bi-LSTM, At-BiLSTM, and Transformer learning, whereas the hybrid methods combine ACMD with these standalone methods. The proposed method demonstrates lower errors as compared to the other approaches with optimal performance in terms of mean absolute error (MAE). Specifically, the MAE observed for load, solar power and wind power are 0.078kW, 5.304kW and 0.426kW, respectively. In addition, the proposed method shows an improvement in forecasting accuracy of nearly 18% with respect to the nearest MAE achieved using Attention-BiLSTM method. The forecast model for load forecasting (LF) and RES forecasting is tested on the data collected for Rye microgrid. The proposed forecast model is also tested on the dataset collected from Panama City.