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The accurate operation and planning of power plants depend heavily on the ability of utilities to estimate load demand, which also enables them to allocate resources more efficiently and guarantee a steady flow of electricity. Modern models have limited relevance in real-world situations and demonstrate severe non-linearity in load information from forecasts that are now accessible. But for practical use, the field of energy forecasting needs more flexibility, enhanced prediction accuracy, and resilience. This study proposes a brand-new method for anticipating short-term load demand that has been uniquely developed for Bangladesh. An extensive dataset made up of historical load demand data, weather factors, and Bangladesh-specific calendar data is used to train and test the model that is suggested. The dataset spans six years of load demand data, covering several seasonal trends. The proposed model uses a parallel CNN-BiLSTM architecture. In this architecture, the BiLSTM module keeps track of temporal relationships and learns long-term trends, while the CNN module pulls out spatial features from the merged dataset. The parallel nature of the model enables it to capture the intricate correlations visible in the load demand time series data. The model also makes use of parallel processing's advantages, which boost the effectiveness of computation. Based on the results of experiments, the proposed CNN-BiLSTM hybrid computational neural network system is better at making accurate predictions than baseline models and traditional forecasting methods. The model successfully captures short-term load demand changes, enabling providers to build facilities, balance load, and schedule resources accordingly. The Mean Absolute Percentage Error (MAPE) and R-squared score, which surpass the existing state-of-the-art models, were taken into consideration for evaluation. [ABSTRACT FROM AUTHOR]

Electric load forecasting, also known as Probabilistic Load Forecasting (PLF), has played a role in the electric power industry. Forecasting the electricity consumption in business is necessary for planning power system operations, stability, and energy trading. Many business entities, such as commercial airports, require electric load forecasting to meet service and regulatory needs. Therefore, forecasting is needed to become a reference in determining strategic management energy. This research aims to forecast the electricity consumption of Soekarno-Hatta International Airport using the Seasonal Autoregressive Integrated Moving Average (SARIMA) model. The study uses daily historical data collected from airport operator companies from 01 January 2022 to 31 December 2022 to build and evaluate the model's performance. The findings show that the SARIMA model (1,1,1)(0,1,1)7 has the best model accuracy with a MAPE of 4.62%. The study conclusions highlight the potential of the model to support energy management practices at Soekarno Hatta International Airport and other similar facilities. [ABSTRACT FROM AUTHOR]

The current era with very advanced technology, most of which use electricity as an energy source, research is needed for the growth of electrical energy needs. One of them is in the city of Banyuwangi which is one of the cities in East Java that is experiencing rapid development in terms of the industry which automatically affects the growth of electrical energy needs. In this case, electrical load forecasting has an important role in the distribution of the electricity network to maintain a balance between demand and supply of electricity, which research has never been done in the city of Banyuwangi. Therefore, an accurate method is needed to predict the electrical load. So in this study using the Autoregressive Integrated Moving Average (ARIMA) method. The ARIMA method is the fully independent variable in making forecasts. The advantage of this method is that it can accept all types of stationary model data. This study takes medium-term load forecasting from January 2015 to December 2019 for the period 2020. From this research, the error value or MAPE will also be obtained. The beginning of data processing from this research is by looking at the time series plot data, stationary data invariance, and stationary data on average. The results of the three aspects indicate that the data is still not stationary. So it is necessary to transform the data. On the average stationery, it is necessary to make a distinction to obtain stationary data. Next, determine the ARIMA model using ACF for order q and PACF for order p. The result is 2 lags that are out of bounds from the partial image and 1 lag that is out of bounds from the autocorrelation image, because differencing is only done 1 time, the possible models are (2,1,1) (2,1,0) (1,1, 1) (1,1,0) (0,1,1) and then testing the parameters. [ABSTRACT FROM AUTHOR]

In energy management, providing accurate findings to anticipate electrical load consumption is critical. The goal of this study is to develop methods for predicting electrical load, including artificial intelligence, neural network, ARIMA models, Bayesian models, and regression models. This paper proposes a new support vector regression application with the chaotic algorithms for electrical load forecasting in the southern region (Basra, Maysan, Dhi Qar, Muthanna), When choosing three parameters of a model (SVR) using evolutionary algorithms, you often encounter problems of early convergence, slowly reaching the solution of global optimization or falling into local optimization, and to overcome early local optimization in determining three parameters of the model (SVR) a chaotic algorithm is used. The 1980-2019 electrical load was used as a data set, and the results (CGASVR) were compared with (CPSOSVR) and (CIASVR) to choose the best form of electrical load forecasting where results show that the CGASVR model is more superior and efficient based on MSE, MAE, MAPE and MPE. [ABSTRACT FROM AUTHOR]

In order to anticipate the impact of external problems, a review of the outlook for rubber commodities and forecasting of rubber commodities in the short term is carried out to determine policies that can be taken to maintain the sustainability of rubber industry in Indonesia. The research was conducted using the desk study method, using secondary data on rubber statistics. The data used include annual and quarterly time series data in the last 5 – 20 years. The analytical method used is descriptive analysis method and forecasting analysis using the ARIMA model. The results of the analysis show that during the 2016-2020 period, the area of rubber showed an increasing trend, with an average growth of 1.37% per year. However, rubber production shows a declining trend at a rate of 3.73%/year. Consumption performance, also decreased by 0.40% per year. The same thing happened to the performance of exports and imports, which also declined by 1.62%/year and 0.70%/year, respectively. The results of the forecasting analysis show that natural rubber production is projected to increase to 1.6 million tons in the second semester of 2021, and will continue to increase in 2022 to 3.3 million tons. Meanwhile, rubber consumption is projected to increase to 330 thousand tons in the second semester. From the results of this analysis, it is necessary to make policy proposals to the government in order to allocate research funds for high yielding rubber clones and resistant to rubber plant diseases; to increase the absorption of domestic rubber consumption as well as the export of rubber; also to increase the program to strengthen the business guarantees for Processing and Marketing Unit of raw rubber material (UPPB)/ farmer corporations as an effort to increase the quality of rubber processing materials and improve the welfare of farmers. [ABSTRACT FROM AUTHOR]

Since seasonal data incorporates a seasonal cycle, forecasting seasonal data differs from forecasting ordinary time series data. Because of its utility in forecasting a linear relationship with other factors, Holt-Winter's model has been frequently employed in load forecasting. However, Holt-has Winter's the drawback of having difficulty modeling a nonlinear connection between the variables and influencing factors. On the other hand, the neural network model is an excellent model for representing nonlinear data. As a result, a combination of Holt-Winter's and NN models is proposed in this work to anticipate future load demand. This hybrid model is then compared to the Holt-Winter and NN models to assess how well it performs. As a performance metric, the RMSE and MAE are utilized, and a fractional residual plot is presented to visualize the error graphically. This model, based on the findings, provides a better prognosis than the other two models. [ABSTRACT FROM AUTHOR]

Nowadays the problem of the efficient use of available energy resources is urgent. It causes the development of energy-saving technologies, including using renewable energy sources and their implementation in existing energy systems. Decentralized electricity generation becomes more expanded. Smart Grids are designed to provide real-time data management for the balance between electricity demand and generation level. To operate and maintain the Smart Grid decision support tools aimed to achieve grid reliability by reducing peak demands and increasing energy efficiency are designed. Decision-making support on the energy management of the hybrid power grid requires in particular determination of the level of electricity consumption, which will determine the effective mode of the hybrid power grid operation. The aim of the study is to compare different techniques and develop forecasting models of electricity consumption with high accuracy. Electricity consumption depends on many variables such as consumer load, environmental conditions, etc. It was considered such forecasting techniques as regression analysis, fuzzy regression analysis and machine-learning based models. The dataset for forecasting was created from archive data of the gas station daily electricity consumption. Autoregressive forecasting models, exponential smoothing models, and neural network models were built for short-term electricity consumption forecasting. The accuracy of proposed models was compared by MAPE criteria. The developed forecast models are possible to obtain a daily schedule of electricity consumption for the next months, as well as an operational forecast for the next day. Forecasting results are used in the decision support system on the operation of the power grid with renewable energy sources. [ABSTRACT FROM AUTHOR]

The paper deals with the forecast of electric power consumption in the region in order to increase cost-efficiency of the utility. The relevance of the topic was vividly demonstrated in 2020, when enterprises - consumers of electricity partially stopped the activity, and the excess supply was observed on electricity market. Under such conditions the concept of daily load regulation was introduced, when the System regulator defines electricity amount required for the market. Issues concerning daily load shaping as well as consumption forecast to increase cost effectiveness of the utility, main tasks to be solved under conditions of daily regulation as well as forecast quality impact on economic efficiency of the company were considered. [ABSTRACT FROM AUTHOR]

This paper uses intraday electricity demand data from Mahakam-East Kalimantan city as the basis of an empirical comparison of univariate methods for prediction up to a day-ahead. A notable feature of the time series is the presence of both an intraday and an intraweek seasonal cycle. The forecasting methods considered in the study include: Double seasonal ARIMA modeling of the daily demand profiles. The Double seasonal ARIMA methods performed well, the method that consistently performed the best was the double seasonal. [ABSTRACT FROM AUTHOR]

In these days, tool wear is a major concern in hard turning process. Though the hybrid machining and thermally enhanced machining methods relieved the burden of the Industry to some extent, tool wear studies are more vital in order to improve the productivity. This research work mainly focuses on such studies wherein the influence of cutting parameters and tool temperature on tool wear are investigated both experimental and analytical procedures. The work material selected for the process was high-chrome white cast iron (HCWCI) and cutting tool as multi-layer hard coated carbide inserts. The experimental trials were carried out in accordance with the Taguchi's techniques. Second order regression was established to build the linear relationship among the process parameters and its responses. The research study reveals that flank and crater wear are the dominant wear patterns appeared in the process. The influencing parameters and their interactive effects on tool wear are also presented in graphical form which may useful while choosing the process parameters for better tool life. [ABSTRACT FROM AUTHOR]

In recent days, road traffic management and congestion control has become major problems in any busy junction in Hyderabad city. Hence short term traffic flow forecasting has gained greater importance in Intelligent Transport System(ITS). Artificial Neural Network(ANN) models have been fruitfully applied for classification and prediction of time series. In this paper, an attempt has been made to model and forecast short-term traffic flow at 6.no. junction in Amberpet, |oHyderabad, Telangana state, India applying Neural Network models. The traffic data has been considered for peak hours in the morning for 8A.M to 12 Noon, for 5 days. Multilayer Perceptron (MLP) network model is used in this study. These results can be considered to monitor traffic signals and explore methods to avoid congestion at that junction. [ABSTRACT FROM AUTHOR]

At present, the short-term power load prediction model generally uses the traditional wavelet neural network for prediction, which utilizes the gradient descent algorithm. However, it has the problems of sensitivity to the initial value and low prediction accuracy. To address this issue, we build a novel short-term power load prediction model leveraging the Wavelet Neural Network (WNN) base on the Comprehensive Improved Shuffled Frog Leaping Algorithm (CSFLA). By using this prediction model, we firstly conduct distributed storage and processing of a large amount of preprocessed historical load data, and then parallelize the processed historical load data by using MapReduce programming framework and WNN to obtain the prediction results. In the experiments, simulation results demonstrate that the proposed prediction model has high accuracy, strong adaptability and excellent parallel performance. [ABSTRACT FROM AUTHOR]

Nowadays wind power forecasting research is becoming more and more important in economic operation and safety management of power systems. Moreover, with a huge increase of data availability, data-driven models, especially for short-term forecast, represent a good compromise between precision and computational loads. This paper deals with a data-driven algorithm consisting of a combination of three main steps: gis-based data collection and pre-treatment, classification wind turbines step (Twostep cluster analysis) and short-term wind power forecast with artificial neural networks (ANN). The wind turbines were geo-referenced, the initial variables (single turbines power output, wind direction and speed) were pre-processed, a recursive optimized cluster analysis (CA) was performed to obtain a simplified mathematical structure for an artificial neural network. A windfarm (48 MW) placed in the eastern part of Sicily (Italy), was tested as case study. It consists of two different sites each one with 28 wind turbines at different rotor heights and different orography. The time series dataset consists of almost four years data (sampling time of 10 minutes). The consequent simplified mathematical structure leaded to perform good results in in a short-term wind power output forecast for both sites of the case study. [ABSTRACT FROM AUTHOR]

On the basis of the previously developed model of the short-term prediction of the variations in the length of day and in Universal time, the accuracy aspects of taking into account intra-day variations in the angular velocity of the Earth axial rotation are studied. It is shown that the contribution of the Universal Time intraday variations model to the accuracy of the dUT1 forecast depends on the forecast accuracy of the interdiurnal model, and the more accurate it is, the more precise prediction the interdiurnal model can make. In the case of low accuracy in a short-term forecast, taking into account intra-day variations can lead to deterioration in the prediction accuracy. [ABSTRACT FROM AUTHOR]

Solar power forecasting plays a critical role in power-system management, scheduling, and dispatch operations. Accurate forecasts of direct normal irradiance (DNI) are essential for an optimized operation strategy of concentrating solar thermal (CST) systems, particularly during partly cloudy days, due to solar intermittency. In this work, short-term forecasts from the radiative scheme McRad (Cycle 41R2) included in the Integrated Forecasting System (IFS), the global numerical weather prediction model of the European Centre for Medium-Range Weather Forecasts (ECMWF), together with in-situ ground-based measurements, are used in a simulated linear focus parabolic-trough power system through the System Advisor Model (SAM). Results are part of a preliminary analysis concerning the value of DNI predictions from the IFS for operation improvement of a CST system with similar configurations as the Andasol 3 CST power plant. For a 365-day period, the present results show high correlations between predictions of energy to grid based on measurements and IFS forecasts mainly for daily values (≈0.94), while lower correlations are obtained for hourly values (≈0.88), due to cloud representation of the IFS during overcast periods, leading to small deviations with respect to those from measurements. Moreover, to measure the forecasting skill of the IFS, daily and hourly skill scores based on local measurements and a persistence model are obtained (≈0.66 and ≈0.51, respectively), demonstrating that the IFS has a good overall performance. These aspects show the value that forecasted DNI has in the operation management of CST power systems, and, consequently, in the electricity market. [ABSTRACT FROM AUTHOR]

High voltage (HV) infrastructures are growing due to the corresponding growth in industries and population. Under certain extreme weather, the required electrical load demands might exceed the generations’ capacity, which forces the network operator to execute the load shedding to avoid unwanted blackout. The increase deployments in renewable energy micro-grids offers an opportunity to aid with the load shedding risks. The paper addresses the embedded generation system and how it can be utilized to mitigate the requirements of load shedding. Furthermore, the study also focusses on the complex power transfer with and without the aid of embedded generation with energy storage. Case study is also included. [ABSTRACT FROM AUTHOR]

The research describes in this paper aims to help improve the performance of a Hydro Power plant operation and planning by analyzing the performance of its power loads. The amount of electrical energy used at certain times cannot be calculated exactly. This can lead to a lack of electricity supply on the consumer side if the generated power is less than the need electrical energy required. Increased electricity needs can also be cause problems to the quality of electric power that is delivered respectively. To overcome things, it is necessary to have a proper electric power system operation plan reliable through forecasting the electrical load in the future. In this study, carried out load forecasting for power system planning and operation of Larona Hydro Power Plant, in smelter plant Sorowako propose an Artificial Neural Network (ANN) method. The ANN was implemented using tools of MATLAB. The structure of the ANN is MLP (Multi-layer Perceptron). The load forecasting conducted in simulation, proceed the data by constructing and train the neural network with this data. After the validation of neural network error rate, the network function used to estimate a short-term prediction with determination predictor parameters namely number of learning input, activation function, and learning rate. Error was calculated as MAPE (Mean Absolute Percentage Error), the model selection criteria are based on the best RMSE values with the smallest MAPE value, and with error of about 0.957% this paper was successfully carried out. [ABSTRACT FROM AUTHOR]

Building reliable electricity consumption forecasts is of particular importance for a number of reasons: technological, economic and environmental. The high quality of planning contributes to the formation of an effective operation mode of the Unified Energy System thereby minimizing the consumption of energetic resources and economic costs of forcing the “generation-consumption” equilibrium. The task of the forecasting is computationally complex, and the development of a cloud solution that will assist in its solution seems expedient. This paper discusses the key ideas and principles that formed the basis for the creation of such a software product and also focuses on the details of its practical implementation. The developed cloud service automates the process of planning electricity consumption for subsequent acquisition in a liberalized market. The cloud system is based on a time series subsystem that solves a wide range of tasks. They are storage, manipulation with series, tracking data modifications and much more. A domain-specific language was created to formalize the nature of the relationship between varieties of time series and a description of the calculation rules. The most important component of the cloud solution being developed is the forecasting module, whose principle of operation is to launch a set of predictive models and build the final forecast based on the results of calculations. Prediction models are implemented as stored subroutines in R and Python languages, and the final assumption of the system is carried out either as a competitive selection of the best model based on the retrospective accuracy indicators (MAPE is taken as an estimate) for similar periods, or by weighted voting applied to the models’ results. Mainly developed product allows maintaining a high level of planning quality, providing a final forecast with MAPE within 5%. [ABSTRACT FROM AUTHOR]

Taking a commercial district as the research object, on the basis of historical energy consumption, a model of cooling load prediction is set up. Firstly, the original method of cooling load prediction is introduced. Then the cooling load indexes are corrected by using the historical energy consumption data. Finally, re-forecasting the cooling load of the commercial district again. The method can be used for cooling load forecasting of building as a guide. [ABSTRACT FROM AUTHOR]

Aiming at the character of electrification railway, the paper mainly studies the problem of load prediction in electrification railway. After the preprocessing of data, and the similar days are separated on the basis of its statistical characteristics. Meanwhile the accuracy of different methods is analyzed. The paper provides a new thought of prediction and a new method of accuracy of judgment for the load prediction of power system. [ABSTRACT FROM AUTHOR]

The article compares a variety of time series models and combines the characteristics of power load forecasting. Then, a light load forecasting model based on ARMA model is established. Based on this model, a light rail system is forecasted. The prediction results show that the accuracy of the model prediction is high. [ABSTRACT FROM AUTHOR]

Short-term forecasts have recently gained an increasing attention because of the rise of competitive electricity markets. In fact, short-terms forecast of possible future loads turn out to be fundamental to build efficient energy management strategies as well as to avoid energy wastage. Such type of challenges are difficult to tackle both from a theoretical and applied point of view. Latter tasks require sophisticated methods to manage multidimensional time series related to stochastic phenomena which are often highly interconnected. In the present work we first review novel approaches to energy load forecasting based on recurrent neural network, focusing our attention on long/short term memory architectures (LSTMs). Such type of artificial neural networks have been widely applied to problems dealing with sequential data such it happens, e.g., in socio-economics settings, for text recognition purposes, concerning video signals, etc., always showing their effectiveness to model complex temporal data. Moreover, we consider different novel variations of basic LSTMs, such as sequence-to-sequence approach and bidirectional LSTMs, aiming at providing effective models for energy load data. Last but not least, we test all the described algorithms on real energy load data showing not only that deep recurrent networks can be successfully applied to energy load forecasting, but also that this approach can be extended to other problems based on time series prediction. [ABSTRACT FROM AUTHOR]

In order to improvement problems that the computational complexity and the accuracy is not high in load decomposition, a load decomposition method based on the maximum a posteriori probability is proposed, the electrical equipment steady-state current is chosen as load characteristic, according to the Bayesian formula, all the electric equipment's' electricity information value can be acquired at a time exactly. Experimental results show that the method can identify the running state of each power equipment, and can get a higher decomposition accuracy. In addition, the data used can be collected by the common smart meters that can be directly got from the current market, reducing the cost of hardware input. [ABSTRACT FROM AUTHOR]

In order to ensure the scientific nature of optimization about power system, it is necessary to improve the load forecasting accuracy. Power system load forecasting is based on accurate statistical data and survey data, starting from the history and current situation of electricity consumption, with a scientific method to predict the future development trend of power load and change the law of science. Short-term load forecasting is the basis of power system operation and analysis, which is of great significance to unit combination, economic dispatch and safety check. Therefore, the load forecasting of the power system is explained in detail in this paper. First, we use the data from 2012 to 2014 to establish the partial least squares model to regression analysis the relationship between daily maximum load, daily minimum load, daily average load and each meteorological factor, and select the highest peak by observing the regression coefficient histogram Day maximum temperature, daily minimum temperature and daily average temperature as the meteorological factors to improve the accuracy of load forecasting indicators. Secondly, in the case of uncertain climate impact, we use the time series model to predict the load data for 2015, respectively, the 2009-2014 load data were sorted out, through the previous six years of the data to forecast the data for this time in 2015. The criterion for the accuracy of the prediction is the average of the standard deviations for the prediction results and average load for the previous six years. Finally, considering the climate effect, we use the BP neural network model to predict the data in 2015, and optimize the forecast results on the basis of the time series model. [ABSTRACT FROM AUTHOR]

This paper examines the influence of aggregated power output fluctuation of photovoltaic power generation system (PVS) on the power system frequency, focusing on ramp-events of aggregated PVS power output. A numerical simulation model of economic load dispatching control (EDC) and load - frequency control (LFC) is utilized together with a PVS power output forecasting model and a unit commitment (UC) scheduling model developed in our preceding study. As a result, in the case of ramp event with long duration and rapid ramp rate, the frequency deviation from the acceptable rage occurs when the power output of controllable generators with high load following capability reaches to upper / lower limit even though the power output change of low load following capability generators is still available. If the load dispatching scheme is temporary switched from the conventional EDC using an equal incremental fuel cost rule to, for example, the dispatching based on the capacity without the consideration of fuel cost, the aggregated load-following capability can be preserved, avoiding the frequency deviation. [ABSTRACT FROM AUTHOR]

Demand response has attracted much attention in recent years. It has been identified as one of the solutions to overcome electricity network equilibrium problems related to peak consumptions and renewables intermittency. From all the different consumption sectors, industrial electricity consumptions are of particular interest due to the magnitude they represent. Thus, industrial consumptions (equipments, workshops, etc.) can be considered as resources for demand response programs to be integrated in balancing the electricity network. In order to be able to integrate these consumptions, it is necessary to standardize the procedures to evaluate their availability as well as forecasting their behavior. The standardization of the procedures shall focus on the program constraints in which the resource is valued, but most importantly, in ensuring the safety of the electricity network. This research paper focuses in three industrial usages with different behaviors. For this purpose, three different forecasting techniques, adapted to the data and operational constraints, are used. Specific performance indicators called "trust" parameters are proposed and calculated for different times of the day for the different usages. The different forecasting techniques show similar performances depending mainly on the data. The estimated trust parameters allow evaluating the probability for a specific consumption of not complying with the operational constraints. [ABSTRACT FROM AUTHOR]

Many activities such as economic, education and manafucturing would paralyse with limited supply of electricity but surplus contribute to high operating cost. Therefore electricity load forecasting is important in order to avoid shortage or excess. Previous finding showed festive celebration has effect on short-term electricity load forecasting. Being a multi culture country Malaysia has many major festive celebrations such as Eidul Fitri, Chinese New Year and Deepavali but they are moving holidays due to non-fixed dates on the Gregorian calendar. This study emphasis on the performance of fuzzy approach in forecasting electricity load when considering the presence of moving holidays. Autoregressive Distributed Lag model was estimated using simulated data by including model simplification concept (manual or automatic), day types (weekdays or weekend), public holidays and lags of electricity load. The result indicated that day types, public holidays and several lags of electricity load were significant in the model. Overall, model simplification improves fuzzy performance due to less variables and rules. [ABSTRACT FROM AUTHOR]

Currently, Chinese power load prediction is highly focused; the paper deeply studies grey prediction and applies it to Chinese electricity consumption during the recent 14 years; through after-test test, it obtains grey prediction which has good adaptability to medium and long-term power load. [ABSTRACT FROM AUTHOR]

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At present, the industrial PV-storage micro-grid has been widely used. This paper presents an optimal allocation model of PV-storage micro-grid capacity considering the important load of industrial users. A multi-objective optimization model is established to promote the local extinction of PV power generation and the maximum investment income of the enterprise as the objective function. Particle swarm optimization (PSO) is used to solve the case of a city in Jiangsu Province, the results are analyzed economically. [ABSTRACT FROM AUTHOR]

The growing burden of the load and the complexity of the power system has had an impact on the need for optimization of power system operation. Optimal power flow (OPF) with optimal location placement and rating of thyristor controlled series capacitor (TCSC) is an effective solution used to determine the economic cost of operating the plant and regulate the power flow in the power system. The purpose of this study is to minimize the total cost of generation by placing the location and the optimal rating of TCSC using genetic algorithm-design of experiment techniques (GA-DOE). Simulation on Java-Bali system 500 kV with the amount of TCSC used by 5 compensator, the proposed method can reduce the generation cost by 0.89% compared to OPF without using TCSC. [ABSTRACT FROM AUTHOR]