Load2Load: Day-ahead load forecasting at aggregated level

In electrical engineering load forecasting have been tried out using traditional forecasting models and artificial intelligence techniques and have become one of the major research fields. An accurate and efficient Short-term load forecasting (STLF) plays a vital role for economic operational planning of both regulated power systems and electricity markets. To develop a solution/methodology to demand forecast (Hourly load forecast) and by incorporating weather conditions. However, STLF it is pertinent to understand conventional methods. Therefore, popular conventional methods were implemented to learn methods for STLF. This paper presents Simple Moving Average, Weighted Moving Average, Exponential Moving Average, Auto Regressive and Multiple linear regressions for short term load forecasting. Conventional technique approach is implemented on historical load data for forecasting the load. PGVCL hourly load data used for training and testing is collected from ALDC, Jetpur, Gujarat. Hence hereby in this paper I have compared five conventional methods for STLF and have come out with a result that forecasting errors of time series models gives reasonably accurate hour ahead load forecast.

Priority index considering temperature and date proximity for selection of similar days in knowledge-based short term load forecasting method

Hybrid short-term load forecasting using CGAN with CNN and semi-supervised regression

Short-term electric load and temperature forecasting using wavelet echo state networks with neural reconstruction

With the promotion of power market reform, accurate short-term load forecasting is of great significance for power systems to formulate reasonable production plans and ensure safe operation of the power grid. At present, the power grids have brought great challenges to load forecasting, and due to the need of production, a large number of nonlinear, asymmetric, and impact loads are connected to the power grid, so it is difficult for traditional power load forecasting methods to fully and accurately characterize the load characteristics. In order to learn the complex hidden deep relationship in nonlinear load data and improve the prediction accuracy, this paper proposed a method based on conditional generative adversarial networks (CGANs). This method used a convolutional neural network to construct a generative model and a discriminant model, took the load influencing factor as a condition. Through the game training of the confrontation network, the generation model can learn the mapping relationship between the noise and the real load data, to perform short-term load forecasting. The validation was conducted using a dataset from a certain power plant, and the experimental results showed that the trained CGAN has strong ability to learn load temporal features and has high prediction accuracy in different scenarios. The subsequent work will analyze the characteristics of different types of load data and consider the impact of real-time electricity prices and other factors on load prediction, so as to further improve the prediction accuracy and universality of the model.

Short-term load forecasting plays a vital role for the power system’s safe operation and production arrangements. This paper regards electric load forecasting as a nonlinear time series prediction problem, and establishes the electric load autoregressive prediction model (ARX Model) based on historical load data, approaching ARX model parameters with RBF neural network, estimating model parameters with using a Structured Nonlinear Parameters Optimization Method (SNPOM), proposing cycle prediction method for short-term electric load forecasting. Experimental results show that the method has higher prediction accuracy for short-term electric load forecasting. Keywords: Short-term load forecasting; time series; RBF-ARX model; cycle forecasting; structured nonlinear parameter optimization method

Electricity becoming more and more important for the development of a secure & sustainable energy system. Electricity Demand growth is likely to be anemic. With the rising complexity of energy systems, new ways to use engineering experience and data collecting to increase load forecasting accurately are needed. With the recent advancements in the field of machine learning to leverage big data sets and draw inferences from a vast data set. The electrical load is predicted by using a variety of research strategies, including deterministic and stochastic procedures. This manuscript intends to estimate load demand by utilizing various stochastic methods for Short-Term Load Forecasting (STLF). The dataset is trained using various regression techniques which include a Decision tree, Support Vector machines (SVM), and Ensembles. The performance of the finest regression methodology is analyzed over parameters like RMSE (root mean square error), MSE (mean square error) and, R-squared, MAE (mean absolute error) & training time. The most effective technique is determined by feeding the best machine learning algorithm to various regression learners over data cross-validation of 5 folds and 7 folds. MATLAB platforms are used to implement the methods.

In this paper, a systematic literature review is presented, through a survey of the main digital databases, regarding modelling methods for Short-Term Load Forecasting (STLF) for hourly electricity demand for residential electricity and to realize the performance evolution and impact of Artificial Intelligence (AI) in STLF. With these specific objectives, a conceptual framework on the subject was developed, along with a systematic review of the literature based on scientific publications with high impact and a bibliometric study directed towards the scientific production of AI and STLF. The review of research articles over a 10-year period, which took place between 2012 and 2022, used the Preferred Reporting Items for Systematic and Meta-Analyses (PRISMA) method. This research resulted in more than 300 articles, available in four databases: Web of Science, IEEE Xplore, Scopus, and Science Direct. The research was organized around three central themes, which were defined through the following keywords: STLF, Electricity, and Residential, along with their corresponding synonyms. In total, 334 research articles were analyzed, and the year of publication, journal, author, geography by continent and country, and the area of application were identified. Of the 335 documents found in the initial research and after applying the inclusion/exclusion criteria, which allowed delimiting the subject addressed in the topics of interest for analysis, 38 (thirty-eight) documents were in English (26 journal articles and 12 conference papers). The results point to a diversity of modelling techniques and associated algorithms. The corresponding performance was measured with different metrics and, therefore, cannot be compared directly. Hence, it is desirable to have a unified dataset, together with a set of benchmarks with well-defined metrics for a clear comparison of all the modelling techniques and the corresponding algorithms.

Short-Term Load Forecasting (STLF) is a fundamental component in the efficient management of power systems, which has been studied intensively over the past 50 years. The emerging development of smart grid technologies is posing new challenges as well as opportunities to STLF. Load data, collected at higher geographical granularity and frequency through thousands of smart meters, allows us to build a more accurate local load forecasting model, which is essential for local optimization of power load through demand side management. With this paper, we show how several existing approaches for STLF are not applicable on local load forecasting, either because of long training time, unstable optimization process, or sensitivity to hyper-parameters. Accordingly, we select five models suitable for local STFL, which can be trained on different time-series with limited intervention from the user. The experiment, which consists of 40 time-series collected at different locations and aggregation levels, revealed that yearly pattern and temperature information are only useful for high aggregation level STLF. On local STLF task, the modified version of double seasonal Holt-Winter proposed in this paper performs relatively well with only 3 months of training data, compared to more complex methods.

Short-term load forecasting is generally applied in power system real-time dispatching and day-ahead generation planning, which is of great significance for power system economic dispatching and safe operation of the system. Much research on short-term load forecasting using neural networks has been carried out at home and abroad. However, how to obtain the optimal network and structure evolution accurately and quickly has evolved into a key challenge for short-term load forecasting, because the prediction effect of the neural network method is more easily affected by the existing network structure and parameters, and the personality difference of the prediction object itself makes it difficult for the parameters to be reused. Aiming at this problem, this paper proposes a TPE-based neural network structure design and parameter optimization method for short-term load forecasting. This method realizes fast optimization of parameters of different types, structures, parameters, and input and output of deep neural networks, and is widely applied in Guangxi power grid. It is verified by an actual example that the method can effectively improve prediction accuracy.

Accurate short-term load forecasting (STLF) is critical to the operation and control of the power grid. In the literature, deep learning techniques have shown its superiority for accurate load forecasting over other methods due to its strong regression ability. However, the training process of the deep learning systems is time-consuming providing that they contain a large number of parameters due to the complex structure. This paper presents a novel broad learning system (BLS) approach for load forecasting. The BLS, which is established as a flat network, is an alternative to the deep learning system by expanding the structure in a wide sense. The expanded broad structure of BLS ensures its strong approximation capability of nonlinear mapping. Moreover, the BLS has a much faster training process compared to deep learning due to its simple structure. The proposed method is tested with an open dataset at low-voltage level from Australia and the testing results demonstrate the effectiveness of the proposed method.

Accurate Short term load forecasting (STLF) plays a significant role in the management of power system of countries and regions on the grounds of insufficient electric energy for increased need. This paper presents an approach of back propagation neural network with rough set (RSBP) for complicated STLF with dynamic and non-linear factors in order to develop the accuracy of predictions. Through attribution reduction based on variable precision with rough set, the influence of noise data and weak interdependency data to BP is avoided so the time taken for training is decreased. Using load time series from a practical system, we tested the accuracy of forecasting in specific days with comparison.

Load forecasting is used primarily to predict future loads for a particular system over a given time period. Short-term loads are typically treated as variable elements influenced by factors such as historical load information and weather datasets, including precipitation, wind speed, and temperature. A precise forecasting with an individual model is almost impossible. The primary challenge for utility companies worldwide is accurately forecasting energy consumption. Accurate short-term load forecasting (STLF) is a cornerstone of smart grid operation, enabling demand-side management (DSM), demand response programs, and efficient integration of distributed energy resources. This study proposes a fuzzy time series (FTS)-based methodology for residential electricity consumption forecasting at hourly, daily, and weekly scales. By addressing overfitting during data partitioning and refining the fuzzification process, our approach improves prediction accuracy compared to traditional FTS models. Simulation results using real consumption data demonstrate up to 40% improvement in hourly forecasting and up to 58% and 84% improvements in daily and weekly forecasts, respectively. These results highlight the potential of FTS-based models to enhance residential demand forecasting, reduce peak-demand uncertainty, and support grid operators in achieving more resilient, flexible, and sustainable smart grid systems.

Short-term load forecasting is an important component of grid economic dispatching and the forecasting error directly affects the economy of grid operation. Different from wide area power grid, it is more difficult to implement short-term user load forecasting, especially existing missing data. The feature model about user load is constructed by analyzing the static, dynamic and distributed characteristics of user load. Then, based on gradient boosted decision trees and similar training samples, a short-term user load filling and forecasting model is proposed. Through predicting different types of user load in short-term, to analyze filling model about missing load data is helpful to improve the accuracy of short-term user load forecasting. And, the forecasting accuracy and its stability of the proposed model are validated.

This paper aims to develop a load forecasting method for short-term load forecasting, based on an adaptive two-stage hybrid network with self-organized map (SOM) and support vector machine (SVM). In the first stage, a SOM network is applied to cluster the input data set into several subsets in an unsupervised manner. Then, groups of 24 SVMs for the next day's load profile are used to fit the training data of each subset in the second stage in a supervised way. The proposed structure is robust with different data types and can deal well with the nonstationarity of load series. In particular, our method has the ability to adapt to different models automatically for the regular days and anomalous days at the same time. With the trained network, we can straightforwardly predict the next-day hourly electricity load. To confirm the effectiveness, the proposed model has been trained and tested on the data of the historical energy load from New York Independent System Operator.