A novel forward operator-based Bayesian recurrent neural network-based short-term net load demand forecasting considering demand-side renewable energy

In remote regions, microgrids are increasingly recognized as dependable electricity sources, underscoring the necessity for precise short-term load demand forecasts to ensure efficient microgrid management. This study assessed three forecasting methodologies—Ridge Regression (RG), Autoregressive Integrated Moving Average (ARIMA), and Support Vector Machine (SVM) regression—for predicting electricity load demand in a microgrid at the University of Jos in Nigeria. The objective was to achieve accurate load demand forecasts within a specified timeframe. Leveraging 6 years of historical load data from the Jos Electricity Distribution Company (JEDC) and weather data from the Nigerian Meteorological Agency (NIMET), the research demonstrated that both RG and ARIMA outperformed SVM, exhibiting lower Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and Mean Absolute Percentage Error (MAPE) values. Specifically, RG recorded an RMSE of 256.414 (representing a reduction of 11.1% compared to SVM), MSE of 65,748.170 (a decrease of 8.9% compared to SVM), and MAPE of 37.730 (a decrease of 12.0% compared to SVM), while ARIMA displayed an RMSE of 215.820 (a decrease of 20.5% compared to SVM), MSE of 46,580.220 (a decrease of 21.4% compared to SVM), and MAPE of 30.210 (a decrease of 32.9% compared to SVM). In contrast, SVM yielded an RMSE of 289.419, MSE of 83,763.112, and MAPE of 45.482. The study concluded that ARIMA outperforms both RG and SVM in accurate microgrid load forecasting, emphasizing the importance of selecting suitable forecasting techniques in energy resource allocation decisions.

Accurate forecasting of demand load is momentous for the efficient economic dispatch of generating units with enormous economic and reliability implications. However, with the high integration levels of grid-tie generations, the precariousness in demand load forecasts is unreliable. This paper proposes a data-driven stochastic ensemble model framework for short-term and long-term demand load forecasts. Our proposed framework reduces uncertainties in the load forecast by fusing homogenous models that capture the dynamics in load state characteristics and exploit model diversities for accurate prediction. The ensemble model caters for factors such as meteorological and exogenous variables that affect load prediction accuracy with adaptable, scalable algorithms that consider weather conditions, load features, and state characteristics of the load. We defined a heuristic trained combiner model and an error correction model to estimate the contributions and compensate for forecast errors of each prediction model, respectively. Acquired data from the Korean Electric Power Company (KEPCO), and building data from the Korea Research Institute, together with testbed datasets, were used to evaluate the developed framework. The results obtained prove the efficacy of the proposed model for demand load forecasting.

In this study, new learning algorithms were employed viz: grey-black-box (GBB) and kernel optimization(K-SVR) for short-term load demand forecasting. The obtained data is randomly categorized into calibration and verification phases. The prediction accuracy of the algorithm is assessed using Correlation Coefficient (R), Coefficient of Determination (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ), Mean Square Error (MSE), and Root Mean Square Error (RMSE). The model combinations were achieved using the input variables feature selection approach. The GBB model has the highest goodness-of-fit (99%) and lowest prediction error (0.0). The results for both GBB, K-SVR models proved promising despite outstanding reliability of GBB model. The results show that the new learning algorithms are capable of forecasting load demand with simultaneous accuracy higher than 80%.

Short-term load forecasting is viewed as one promising technology for demand prediction under the most critical inputs for the promising arrangement of power plant units. Thus, it is imperative to present new incentive methods to motivate such power system operations for electricity management. This paper proposes an approach for short-term electric load forecasting using long short-term memory networks and an improved sine cosine algorithm called MetaREC. First, using long short-term memory networks for a special kind of recurrent neural network, the dispatching commands have the characteristics of storing and transmitting both long-term and short-term memories. Next, four important parameters are determined using the sine cosine algorithm base on a logistic chaos operator and multilevel modulation factor to overcome the inaccuracy of long short-term memory networks prediction, in terms of the manual selection of parameter values. Moreover, the performance of the MetaREC method outperforms others with regard to convergence accuracy and convergence speed on a variety of test functions. Finally, our analysis is extended to the scenario of the MetaREC_long short-term memory with back propagation neural network, long short-term memory networks with default parameters, long short-term memory networks with the conventional sine-cosine algorithm, and long short-term memory networks with whale optimization for power load forecasting on a real electric load dataset. Simulation results demonstrate that the multiple forecasts with MetaREC_long short-term memory can effectively incentivize the high accuracy and stability for short-term power load forecasting.

Short-term demand forecasting for online car-hailing using ConvLSTM networks

This study examines the application of machine learning models to week-ahead load forecasting for power system short-term planning applications. Electrical energy consumption has increased significantly over the last two decades because of rising living standards, economic development, and population growth. For power system planners and operators, short-term load forecasting is necessary to make economic, safe, reliable, and operational planning decisions and to account for variations in energy demand based on the seasonal period. These models are essential for the operation of utilities to meet system short-term adequacy assessment studies. With uncertainties in national, regional, and local economic growth, along with seasonal variability in energy consumption patterns and conservation habits of electricity consumers, utilities may face new challenges in forecasting short-term load demand. This work considers five machine learning models for week-ahead hourly load forecasting based on historical actual load data and expected meteorological features such as humidity, pressure, cloud cover, temperature, and wind speed. The performances of the models are evaluated using real-world data provided on the IEEE DataPort for Electricity Demand Forecasting. The accuracies are assessed using the root mean square error (RMSE) and coefficient of determination (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) metrics. The results show that the Nonlinear autoregressive neural network with exogenous input (NARX) model performs well in predicting week-ahead load demand with an R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> value of 0.99 on the test set and can accommodate seasonal trends in energy consumption.

The accuracy of short-term demand forecasting is critical for real-time operation management of urban rail transit, which largely depends on the choice of time resolution. Although there have been continuous improvements in forecasting models, the basic issue has not been well addressed. In this regard, the predictability of short-term demand in terms of time resolution setting and the corresponding model selection have been addressed in this study. Two methods have been considered: the demand forecasting with the past demand during the same time slot on the same weekday (the same period method); and that with continuous time series demand exactly before the forecasted time slot (time series method). The predictability for these two methods was respectively measured by the similarity of the same period and the stability of the time series. Consequently, the influence of time resolution on the predictability of short-term demand for urban rail transit has been evaluated. With the methods proposed, this study conducted an analysis on five-week smartcard data in the Beijing subway system. Results suggest that the predictability of short-term demand presented remarkable heterogeneity in both time and space. The predictability of demand forecasting at station level has been summarized into different levels, and the corresponding methods can be selected for each class. Generally, to ensure a desirable accuracy, forecasting can be made at a 10-min and 60-min interval on weekdays and weekends, respectively. The same period method works better for the short-term demand forecasting on weekdays. While the time series method performs better for prediction on weekends. As for short-time OD (origin-destination) demand, the time series method with a 10-min interval, which is supplemented by the same period method, can generate acceptable forecasting results. In brief, this study provides suggestions on the time resolution and method selection for short-term demand forecasting.

This work proposes a short-term electrical load demand forecaster for the Nigerian power distribution firms in Abuja, Benin, and Enugu. Using artificial neural network, the forecaster is created. Hour of the day, calendar day, day of the week (Sunday-Saturday), load demand of the previous day, load demand of the previous week, and average load demand of the preceding 24 h are the inputs to the neural network. The historical load demand for 2017–2020 includes hourly resolved dates and load demand for Abuja, Benin, and Enugu distribution firms for training purposes, while data for 2020 was used for testing the algorithm. The results generated a mean average percentage error ranging from 0.16 to 0.35. This forecaster is essential to Nigeria's efforts to expand access to power in accordance with Sustainable Development Goal 7.

Shared bicycle is an emerging industry in recent years. It is an important part of urban transportation system. Its shortterm demand forecasting is of great significance to the supply, management and allocation of shared bicycle resources. The data of shared bikes are crawled to analyse the impact of time and climate characteristics on the demand for shared bikes. The short-term demand of shared bicycles is predicted by long short-term memory neural network. The experimental results showed that the long short-term memory neural network is suitable for the prediction of shared bicycle demand, and the prediction results with climate characteristics are better than those with only time series. Applying this model to predict the short-term demand of shared bicycles can improve the configuration efficiency of shared bicycles. On this basis, it provides a basis for establishing accurate and effective shared bicycle configuration strategy.

Optimal Operations Management of Hybrid Energy Systems Through Short-Term Atmospheric and Demand Forecasts

The conventional load dispatching among thermal units is determined on the basis of the well-known equal incremental fuel cost law but this method essentically fails to take into account shortterm future load demand. This paper presents a novel load dispatching method which takes into account the response capabilities of thermal units and short-term future load demand. This method has been successfully applied to a sample system. From a series of simulation results, it may be concluded that the optimum forecast period is approximately one hour in which the load demand should be forecast for a total of 4 to 6 points. Application of this method to an on-line load dispatching control in every utility is sufficiently possible at the present time and would be in the future.

Efficient energy management is crucial given 2024's 4.1% worldwide electricity demand increase. This urgency emphasizes the necessity for various, sustainable energy sources in distribution grids. Short-term load prediction approaches using probabilistic power generation and energy storage are crucial for energy usage prediction. Urban energy planners use simulation, probability optimization and modelling to create sustainable energy systems. This study offers a novel hybrid model for smart grids: short-term energy load prediction using transfer learning (TL) and optimized lightGBM (OLGBM). Our two-phase solution tackles Short-term Load Forecasting complexities. First, aberrant supplements and quick deviation selection eliminate missing values and identify key features during data pre-processing. Second, TL-OLGBM learns dynamic time scales and complex data patterns with Bayesian optimization of hyperparameters to improve forecasting accuracy. Additionally, our architecture easily combines the newest Smart and Green Technology, enabling energy system innovation. Comparative performance research shows that our technique outperforms similar models in mean absolute percentage error, accuracy and root mean square error. This hybrid model is a reliable short-term energy load forecast solution that fits the dynamic terrain of smart and green technology integration in modern energy systems.

Short-term electricity load forecasting provides a basis for day-ahead energy scheduling. To improve the accuracy of short-term electricity load forecasts and deeply explore the temporal characteristics of load sequences, a method is proposed to extract predictable components of load sequences based on the secretary bird optimization algorithm (SBOA)-optimized successive variational mode decomposition (SVMD). This method decomposes the electricity load sequence into multiple subsequences under different time series. The combined forecasting architecture of the temporal convolutional network (TCN) and the bidirectional long short-term memory network (BiLSTM) is introduced to mine the temporal characteristics of each load component, resulting in short-term load forecasting outcomes. A case study is conducted using the annual electricity load data for the year 2018 from a specific region in Belgium. The experimental results show that the mean absolute error (MAE) of the TCN–BiLSTM model is reduced by 47.8%, 32.8%, and 11.5%, respectively, compared to other models. The root mean square error (RMSE) is reduced by 42.9%, 39.2%, and 11.3%, respectively, and the average goodness of fit R2 is reduced by 9.81%.

Tourism demand forecasting comprises an important task within the overall tourism demand management process since it enables informed decision making that may increase revenue for hotels. In recent years, the extensive availability of big data in tourism allowed for the development of novel approaches based on the use of deep learning techniques. However, most of the proposed approaches focus on short-term tourism demand forecasting, which is just one part of the tourism demand forecasting problem. Another important part is that most of the proposed models do not integrate exogenous data that could potentially achieve better results in terms of forecasting accuracy. Driven from the aforementioned problems, this paper introduces a deep learning-based approach for long-term tourism demand forecasting. In particular, the proposed forecasting models are based on the long short-term memory network (LSTM), which is capable of incorporating data from exogenous variables. Two different models were implemented, one using only historical hotel booking data and another one, which combines the previous data in conjunction with weather data. The aim of the proposed models is to facilitate the management of a hotel unit, by leveraging their ability to both integrate exogenous data and generate long-term predictions. The proposed models were evaluated on real data from three hotels in Greece. The evaluation results demonstrate the superior forecasting performance of the proposed models after comparison with well-known state-of-the-art approaches for all three hotels. By performing additional benchmarks of forecasting models with and without weather-related parameters, we conclude that the exogenous variables have a noticeable influence on the forecasting accuracy of deep learning models.

Accurate short-term electrical load forecasting is crucial for the stable operation of power systems. Given the nonlinear, periodic, and rapidly changing characteristics of short-term power load forecasts, this paper introduces a novel forecasting method employing an Extreme Learning Machine (ELM) enhanced by an improved Dwarf Mongoose Optimization Algorithm (Local escape Dwarf Mongoose Optimization Algorithm, LDMOA). This method addresses the significant prediction errors of conventional ELM models and enhances prediction accuracy. The enhancements to the Dwarf Mongoose Optimization Algorithm include three key modifications: initially, a dynamic backward learning strategy is integrated at the early stages of the algorithm to augment its global search capabilities. Subsequently, a cosine algorithm is employed to locate new food sources, thereby expanding the search scope and avoiding local optima. Lastly, a “madness factor” is added when identifying new sleeping burrows to further widen the search area and effectively circumvent local optima. Comparative analyses using benchmark functions demonstrate the improved algorithm’s superior convergence and stability. In this study, the LDMOA algorithm optimizes the weights and thresholds of the ELM to establish the LDMOA-ELM prediction model. Experimental forecasts utilizing data from China’s 2016 “The Electrician Mathematical Contest in Modeling” demonstrate that the LDMOA-ELM model significantly outperforms the original ELM model in terms of prediction error and accuracy.