A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings

Smart grid (SG) is bringing revolutionary changes in the electric power system. SG is supposed to provide economic, social, and environmental benefits for many stakeholders. A smart meter is an essential part of the SG. Data acquisition, transmission, processing, and interpretation are factors to determine the success of smart meters due to the excess amount of data in the grid. Electricity price and load are considered the most influential factors in the energy management system. Moreover, electricity price and load forecasting performed through data analytics give future trends and patterns of consumption. The energy market trade is based on price forecasting. Accurate forecasting of electricity price and load improves the reliability and management of electricity market operations. The aim of this paper is to explore the state of the art proposed for price and load forecasting in terms of their performance for reliable and efficient smart energy management systems.

The evolution of conventional power systems into smart grids demands intelligent and efficient energy management solutions. Accurate load forecasting is essential for optimal generation scheduling, demand-side management, and system reliability. However, traditional methods often fail to capture the nonlinear and time-varying nature of electrical loads. This work proposes a machine learning–based approach for local load forecasting and grid stability enhancement. Techniques such as Artificial Neural Networks (ANN) and Support Vector Machines (SVM) are utilized to analyze historical load and weather data for improved prediction accuracy. In addition, a predictive control strategy is implemented to maintain system stability by mitigating voltage fluctuations, frequency deviations, and load imbalances. The proposed model dynamically adjusts control actions such as demand response and load shedding. Simulation results demonstrate reduced forecasting errors (MAE, RMSE) and improved voltage and frequency regulation, ensuring enhanced reliability, efficiency, and stable operation of modern smart grid systems. Keywords: Smart Grid; Machine Learning; Load Forecasting; Grid Stability; Predictive Control.

Load forecasting is an essential part of power system planning. This helps in proper and economic operation of power system plant. The problem of accurate load forecasting has become more baffling in current scenario since deregulation came into picture and industrial growth is positive. This brought highly non-linear electric load profile that is difficult to forecast. Artificial Intelligence (AI) based forecasting models are always preferred over conventional models, because it is heuristic and adaptive approach to learn. In this paper, AI based Feed Forward Neural Network (FFNN) is used to evolve half-hourly load forecasting model for a period of three days. This model is tested and validated on five Australian states' load data. Out of these five cases, forecasting accuracy in four cases is confined to 2.7233%; amongst these four cases two show the forecasting error less than 2%. In one case forecasting error is 6.4593%, which is acceptable because the state consumes lesser energy. This shows the appropriateness of the FFNN forecasting model for multi-regional load forecasting.

ESAENARX and DE-RELM: Novel schemes for big data predictive analytics of electricity load and price

Electricity load forecasting and feature extraction in smart grid using neural networks

Application of hybrid computational intelligence models in short-term bus load forecasting

Load forecasting plays a crucial role in fields such as energy management and power system planning. Accurate forecasting can effectively reduce energy costs and enhance the stability and reliability of power systems. Traditional forecasting methods struggle to balance forecasting accuracy and efficiency when dealing with complex and variable load data. Although the Long Short-Term Memory network (LSTM) can address the long-term dependency problem in time series and shows certain advantages in load forecasting, the setting of its initial parameters has a significant impact on the forecasting results, and it is prone to getting trapped in local optima. The Particle Swarm Optimization algorithm (PSO), based on swarm intelligence, has powerful global search capabilities and fast convergence. This paper proposes a load forecasting method based on PSO-LSTM, which uses PSO to optimize the parameters of LSTM, thereby enhancing the generalization ability and forecasting accuracy of the model. Through case-based analysis and comparison, it can be seen that compared with traditional forecasting methods, this method effectively improves the accuracy of load forecasting and provides strong support for the efficient and stable operation of power systems.

In recent years, the energy landscape has undergone significant transformations, characterized by the integration of renewable energy sources, smart grids, and the proliferation of IoT-enabled devices. As a result, the efficient management of energy resources has become paramount, requiring advanced methodologies in load forecasting and clustering. This article presents an enhanced methodology for short-term load forecasting that focuses on load consumption profile recognition within a smart building environment. The methodology is designed to analyze and identify recurring load consumption profiles and measures of sensors, thereby enhancing load consumption profile recognition capabilities within the smart building context. The interaction between single and grouped datasets is explored to enhance the accuracy and interpretability of predictions, contributing to optimized energy consumption and providing valuable information for demand response programs. The default forecasting methods used in the methodology are artificial neural networks and k-nearest neighbors. For comparing results and evaluating the proposed approach, XGBoost is also employed. The dataset is selected from a specific database, and the clustering method, partitioning type, is applied with k-means. The results, validated with error evaluation models and statistics, reveal the advantages of the proposed approach, especially with three clusters, where the results achieved by the Artificial Neural Network are the best. The clustering process, particularly the partitioning type, demonstrates a strong capability in improving load forecasting in smart buildings and helps understand load consumption patterns and achieve energy savings.

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.

This paper aims at explaining how AI can augment smart grids toward improved energy management. Mainly, the research is concerned with using artificial intelligence together in order to optimize energy distribution, minimize the cost of running organizations and finally establish means to harness sustainable energy resources. Four AI algorithms are utilized in this study: During the smart grid, load forecasting, fault detection, and energy optimization, ANN, SVM, GA, and RL approaches can be used. Based on the experimental findings, it is shown that the ANN achieved a 92% forecast precision; on the other hand, the utility of both the SVM and GA algorithms in energy optimization ranged from 18–23%. The RL algorithm returned the best result and cut wastage by 32% and enhanced load balance efficiency. These results demonstrate that AI strategy performs better than the conventional energy management systems, particularly for accommodating renewable generation and dynamic grid loads. By optimising the utilisation of the grid, and utilising artificial intelligence, energy losses are kept to an absolute minimum, thus supplementing sustainable efforts. The study establishes that smart grid using AI technology is the innovative way of enhancing energy management and thus sustainable smart infrastructures. In future work, issues of data security and scalability will be targeted in order to enhance the application of AI in smart grids.

With the deployment of advanced metering infrastructure (AMI), an avalanche of new energy-use information became available. Better understanding of the actual power consumption patterns of customers is critical for improving load forecasting and efficient deployment of smart grid technologies to enhance operation, energy management, and planning of electric power systems. Unlike traditional aggregated system-level load forecasting, the AMI data introduces a fresh perspective to the way load forecasting is performed, ranging from very short-term load forecasting to long-term load forecasting at the system level, regional level, feeder level, or even down to the consumer level. This paper addresses the efforts involved in improving the system level intraday load forecasting by applying clustering to identify groups of customers with similar load consumption patterns from smart meters prior to performing load forecasting.

The load in the power system has become so dynamic these days that needs sophisticated technology to monitor and control the power system. Load forecast shares a major responsibility in doing so. It helps in unit commitment to the grid and also in proper planning of the infrastructure. Even a minute error in the load forecast may cost so much both in terms of finance and reliability. So, it is imperative for electrical engineers to equip the latest methodologies in accurately predicting the demand well in advance. Although the prediction of demand was first talked about way back in 1925, it has seen a major push with the introduction of statistical methods. In recent years, Artificial Intelligence has brought another major shift in restricting the error margin to trivia, thereby precisely predicting the demand. But, out of the various algorithms and methodologies available, it is important to choose the better model with better accuracy and ease of deploying. In this paper, the authors compare as many as 4 different methodologies to conclude the best fit model for a precise short-term load forecast using univariate data. The prediction has been performed using Triple Exponential Smoothing, Explicit Multi Seasonality, Artificial Neural Network, and Long Short-Term Memory techniques.

The growing success of smart grids (SGs) is driving increased interest in load forecasting (LF) as accurate predictions of energy demand are crucial for ensuring the reliability, stability, and efficiency of SGs. LF techniques aid SGs in making decisions related to power operation and planning upgrades, and can help provide efficient and reliable power services at fair prices. Advances in artificial intelligence (AI), specifically in machine learning (ML) and deep learning (DL), have also played a significant role in improving the precision of demand forecasting. It is important to evaluate different LF techniques to identify the most accurate and appropriate one for use in SGs. This paper conducts a systematic review of state-of-the-art forecasting techniques, including traditional techniques, clustering-based techniques, AI-based techniques, and time series-based techniques, and provides an analysis of their performance and results. The aim of this paper is to determine which LF technique is most suitable for specific applications in SGs. The findings indicate that AI-based LF techniques, using ML and neural network (NN) models, have shown the best forecast performance compared to other methods, achieving higher overall root mean squared (RMS) and mean absolute percentage error (MAPE) values.

In this paper, an artificial neural network (ANN) based methodology is proposed to forecast electricity load and price. The performance of an ANN forecast model depends on appropriate input parameters. Parameter tuning of ANN is very important to increase the accuracy of electricity price and load prediction. This is done using mutual information and decision tree. After selecting best features for forecasting, these features are given to forecasting engine working on principles of recurrent neural network (RNN). For simulations, dataset is taken from national electricity market (NEM), Australia. Results show that the methodology has increased the accuracy of electricity load and price forecast. Whereas, the error rate of forecasting is lower than the other models for electricity load and price.

This chapter explores the convergence of sustainability, intelligent methods, and smart grids as a solution to the escalating energy demands and environmental concerns of the 21st century. It investigates how smart grids, empowered by machine learning, artificial intelligence, and other advanced techniques, can create a more efficient and environmentally friendly energy ecosystem. Key applications in load forecasting, demand management, renewable integration, and grid optimization are examined. The chapter acknowledges challenges like resource intensity and data privacy, advocating for robust regulations and responsible development to ensure equitable access to smart grid benefits. Ultimately, it presents a comprehensive analysis of this synergy, aiming to equip readers with the knowledge to navigate the complexities of the energy landscape and build a sustainable energy future.