Accurate Forecast Method Research Articles

ANALISIS PERBANDINGAN METODE PERAMALAN (FORECASTING) PENJUALAN MOTOR LISTRIK PADA CV SANTOSA ABADI MOTOR PELAIHARI

The aim of this study is to identify the most accurate and effective forecasting method for CV. Santosa Abadi Motor Pelaihari in predicting the sales of electric motor products for the upcoming period. This research is a descriptive quantitative study utilizing secondary data, consisting of electric motor product sales data from 2023 through January-June 2024, obtained from the Administration department of CV Santosa Abadi Motor Pelaihari. The analysis involves comparing three forecasting methods: Trend Moment, Semi Average, and Least Square, and evaluating the accuracy of each method using the Mean Absolute Percentage Error (MAPE). The results indicate that the Trend Moment method yields a forecasting error percentage of 71%, the Semi Average method results in 23%, and the Least Square method shows 28%. Based on these findings, it can be concluded that the Semi Average method is more suitable for forecasting electric motor sales at CV Santosa Abadi Motor Pelaihari.

ENHANCING 〖PM〗_(2.5) PREDICTION IN KEMAYORAN DISTRICT, DKI JAKARTA USING DEEP BILSTM METHOD

Worldwide air pollution is a concern, and this is especially true in Indonesia, where most people breathe air that is more contaminated than recommended by the WHO. The concentration of presents notable health hazards. The respiratory system is the primary route of absorption for , allowing it to enter the lung alveoli and enter the bloodstream. Given the significant health risks associated with exposure, accurate forecasting methods are crucial to anticipate and mitigate its effects. Traditional forecasting methods like ARIMA have limitations in handling non-linear and complex patterns. Therefore, an accurate machine learning method is needed to improve forecasting performance. This research employs Deep Bidirectional Long-Short Term Memory (BiLSTM), a deep learning model particularly suited for time series forecasting due to its ability to capture both past and future dependencies in sequential data. To achieve accurate and precise forecasts for predicting concentration levels in Kemayoran District in November , 2023 (24 hours), this research utilized hourly concentration data from May until October , 2023, using Deep BiLSTM. The outcomes demonstrated the efficiency of the model, attaining a Mean Absolute Percentage Error (MAPE) of 17.1540% (training) and 14.2862% (testing) with an 80:20 data split. The optimal parameters, which comprised 24 timesteps, Adam optimizers with a learning rate of 0.001, 16 batch sizes, 1000 epochs, and ReLU activation functions across multiple BiLSTM layers, showcased the model’s effectiveness in forecasting the concentration in Kemayoran District, DKI Jakarta, on November , 2023.

An innovative method for short-term forecasting of blockchain cryptocurrency price

An innovative method for short-term forecasting of blockchain cryptocurrency price

Forecasting Western Corn Rootworm (Diabrotica virgifera virgifera LeConte) Density and Non-Chemical Control of Larvae: A Practical Review

The western corn rootworm (WCR) (Diabrotica virgifera virgifera LeConte; Chrysomelidae) is one of the most significant maize pests in Europe, with farmers spending a substantial amount (approximately 140 EUR) on its control. In the context of climate change, WCRs could pose an even greater threat to EU maize production, particularly as the European Union continues to withdraw an increasing number of effective yet environmentally harmful active agents. Biological control methods have now emerged to the forefront in creating sustainable agriculture. In this review, we carried out an extensive literature analysis on methods for forecasting WCRs and evaluated the practical applicability of the latest non-chemical control methods targeting its larvae. Effective forecasting is essential for successful pest management, enabling informed planning and the selection of the most suitable control methods. Several traditional predicting techniques remain in use today, but recent advancements have introduced modern electronic forecasting units combined with sensor-equipped pheromone and colour traps, as well as thermal sum calculations. Research has demonstrated that crop rotation is one of the most effective methods for controlling WCR larvae. Biological agents, such as entomopathogenic fungi (Beauveria bossiana and Mettarrhyzum anasoplia), entomopathogenic nematodes (Heterorhabditis bacteriophora), and botanical insecticides such as azadirachtin can significantly reduce larval populations and root damage, thereby maintaining infestation levels below the economic threshold. Genetically modified maize plants that produce specific toxins, along with conventional breeding efforts to increase root system regeneration, are also promising tools for the sustainable management of this pest. This review summarizes the solutions for prediction of western corn rootworm infestations and non-chemical control of its larvae. Accurate forecasting methods provide a clear picture of infestation levels in a given area, enabling precisely targeted control measures. In all cases, the control should be directed primarily against the larvae, thereby reducing root damage and reducing the size of the emerging imago population. This review demonstrates that biological control methods targeting larvae can be as effective as pesticides, supporting sustainable pest management.

Estimation of Solar Power Generation Through the Use of Effective Forecasting Algorithms

The unpredictable nature of solar power generation poses significant challenges to energy management, particularly in power grids with high solar penetration, leading to potential imbalances between supply and demand. This has increased interest in developing accurate forecasting methods for photovoltaic power generation. This study focuses on short-term forecasting of solar power generation using MATLAB, based on historical generation trends through three different hybrid models. It uses a multilayer perceptron neural network as the forecasting tool, which is optimized through various algorithms to fine-tune the network's hyperparameters, such as the weights and biases of the input and hidden layers, for optimal performance. The Root Mean Square Error is used as the objective metric to minimize the discrepancy between actual and predicted values. The models use Particle Swarm Optimization (PSO), Imperialistic Competitive Algorithm, and Genetic Algorithm for network optimization. This research analyzes PV generation data from Belgium in July and August 2022, taken at 15-minute intervals, and evaluates model performance using six different error metrics, including Mean Absolute Error. The results show the accuracy and reliability in forecasting solar power generation time series.

An Information Fusion Technique for Prognosticating Future Air Passenger Trends

The aviation industry is constantly changing and to keep up with the trends of air passengers we need predictive models. In this paper, we explore the use of Information Fusion methodologies and classical time series techniques to forecast how many passengers will be traveling by air. Predicting passenger demands is a task, due to various factors that influence travel patterns. The existing models often struggle to capture the dynamics in this field so it's crucial to develop accurate forecasting methods. By leveraging information fusion techniques like smoothing and Autoregressive Integrated Moving Average (ARIMA) our research creates models based on historical data of air passenger volumes. These techniques combine machine learning algorithms and time series analysis to identify dependencies and patterns in the dataset. Through evaluations and comparative analyses, our proposed models demonstrate promising capabilities in forecasting future air passenger volumes. Proof-of-concept experiments based on 5-fold cross-validation demonstrate the efficacy of the proposed approach in capturing underlying trends and seasonality within the dataset.

Research on wind speed behavior prediction method based on multi-feature and multi-scale integrated learning

Wind power is just in place for a major improvement to the power grid in security and economy. More accurate forecast methods are emerged to benefit grid operators and their customers. A wind speed behavior prediction method based on multi-feature and multi-scale integrated learning (MFMS) is developed in this paper. The behavior characteristics are proposed to solve the problem of the temporal and spatial feature extraction distinctly of wind speed. Multi-scale feature fusion and spatial pyramid pooling algorithms are proposed to reduce the loss of micro-space-scale and short-time-scale information. Meanwhile, environmental features and background features are applied to consider the influence of the overall wind farm environment on the wind speed of local wind turbines. The wind speed data of sixteen wind turbines in a wind farm at Zhangjiakou in North China are used to verify the method. Results show that MAPE, RMSE and R 2 of 4-h ultra-short-term wind speed prediction are 6.164%, 0.275 and 0.966, respectively, which are improved by 1.786%–6.757%, 0.051–0.216 and 0.014–0.074 compared with other methods. The method proposed in this paper can be applied to wind speed prediction and provide a guideline for wind farm power prediction. • A multi-step wind speed prediction model of wind turbine is proposed. • Meteorological factors and wind behavior were used as background/foreground feature. • Features are deeply excavated with the shallow feature loss been overcome. • The model efficiency and accuracy is validated by wind data from a wind farm. • The model shows high accuracy in ultra-short-term wind speed prediction.

Probability density function forecasting of residential electric vehicles charging profile

Residential electric vehicle (REV) is an advanced technology with a rapid growth rate in transportation and electric grids. One key challenge in the operation of REVs is the necessity of the accurate, reliable, and practical forecasting method to provide accurate information of the charging profile in the look-ahead hours. In power system, in order to optimize the production and consumption as much as possible, in addition to accurately predicting the amount of electricity consumption, it is necessary for the stability of the grid to take into account the imminent probabilities. This paper presents the main principle of the probability density function forecasting approach in residential electric vehicle (REV) charging profile. To this end, an end-to-end deep learning structure is designed and integrated with kernel density estimation (KDE). The designed network is composed of four major blocks, i.e., convolutional layers to extract full spatial features, gated recurrent unit (GRU) to fully understand the temporal features as a time-efficient version of the gated deep network, an autoregressive (AR) to model the long patterns including battery type, REV type, and number of REVs and kernel density estimator block. Furthermore, to improve the learning ability of the designed network, an attention mechanism is integrated into the design network. The numerical results on the actual REVs (about 348 REVs) demonstrate the effectiveness and superiority of the proposed network through several cases and comparison with several well-known deep and shallow-based methods.

A multi-model fusion based non-ferrous metal price forecasting

Non-ferrous metals play a significant role in social development. It is important for policy makers and entrepreneurs to forecast non-ferrous metals price accurately. However, existing methods are hard to obtain satisfactory results because the fluctuation rule of non-ferrous metal price is increasingly complex. Therefore, it is necessary to develop more accurate and stable forecasting method. In this paper, a multi-model fusion based non-ferrous metal price forecasting method is proposed. The dual-stage signal decomposition algorithm which combines complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and variational mode decomposition (VMD) is innovatively introduced into non-ferrous metal price forecasting. First, CEEMDAN is used to decompose the original price into several subsequences. Second, the most complex subsequence with maximum sample entropy (SE) is further decomposed by VMD. Dual-stage decomposition reveals essential features such as long-term trend and periodic fluctuations hidden in original sequence and thus lowers prediction difficulty . Besides, particle swarm optimization (PSO) is used to select optimal parameters for VMD. Finally, all subsequences are predicted by long short-term memory network (LSTM) and the results are integrated as the final prediction result. In the empirical study of London Metal Exchange (LME)'s copper, aluminum and zinc price, the proposed method is superior to all benchmark methods in terms of RMSE, MAE and MAPE. The results demonstrate that the proposed method is effective and robust. • A multi-model fusion based method is proposed to predict non-ferrous metal price. • CEEMDAN and VMD are innovatively combined to extract complex features. • PSO is used to select the optimal parameters for VMD. • Comparisons demonstrate the superiority of the proposed method.

Prediction of Typhoon Track and Intensity Using a Generative Adversarial Network With Observational and Meteorological Data

To save lives and reduce damage from the destructive impacts of a typhoon, an accurate and fast forecast method is highly demanded. Particularly, predictions for short lead times, known as nowcasting, rely on fast forecasts allowing immediate emergency plannings in the affected areas. In this paper, we propose a generative adversarial network that operates on a single graphics processing unit, to predict both the track and intensity of typhoons for short lead times within fractions of a second. To investigate the effects of meteorological variables on typhoon forecasts, we conducted a parameter study for 6-h track predictions. The results of the study indicate that learning velocity, temperature, pressure, and humidity along with satellite images have positive effects on prediction accuracy. To address the limited access to observational data and facilitate predictions for 12-h intervals, we replaced satellite images with reanalysis data of the total cloud cover and vorticity fields. This replacement led to an increase in data from 76 to 757 typhoons, and it reduced the error of the 6-h track forecasts by 23.5%. The best combination of the parameter study yields track predictions in intervals of 6 and 12 h with the corresponding averaged absolute errors of 44.5 and 68.7 km. Typhoon intensities are predicted by extracting information from generated velocity fields with averaged hit rates of 87.3% and 83.2% for 6- and 12-h interval forecasts, respectively. For typhoons after 1994, tracks and intensities for 12-h intervals are compared to forecasts from the Joint Typhoon Warning Center and Regional Specialized Meteorological Center Tokyo.

Day-ahead Electricity Market Estimation of Finland in 2030

The European Green Deal targets to increase significantly the share of renewable energy sources (RES) in the electricity system by 2030. However, the electricity market is a competed sector and the private investors develop RES capacity based on the profitability of the business plan. The RES profitability depends strongly on the long-term future price of the electricity market, which needs to be estimated. Although several studies focus on developing accurate short-time forecasting methods for the day-ahead market, these methods cannot be used for long-term forecasting due to uncertainty of the required inputs, e.g. weather condition and fuel cost, in a long-term future. In these circumstances, this paper developed a methodology to analyse and estimate the long-term day-ahead electricity market. This research used the data of Finnish electricity market to show the performance of the methodology. However, the same methodology could also be applied to other countries.

An adaptive short-term forecasting method for the energy yield of flat-plate solar collector systems

The number of large-scale solar thermal installations has increased rapidly in Europe in recent years, with 70 % of these systems operating with flat-plate solar collectors. Since these systems cannot be easily switched on and off but directly depend on the solar radiation, they have to be combined with other technologies or integrated in large energy systems. In order to most efficiently integrate and operate solar systems, it is of great importance to consider their expected energy yield to better schedule heat production, storage and distribution. To do so the availability of accurate forecasting methods for the future solar energy yield are essential. Currently available forecasting methods do not meet three important practical requirements: simple implementation, automatic adaption to seasonal changes and wide applicability. For these reasons, a simple and adaptive forecasting method is presented in this paper, which allows to accurately forecast the solar heat production of flat-plate collector systems considering weather forecasts. The method is based on a modified collector efficiency model where the parameters are continuously redetermined to specifically consider the influence of the time of the day. In order to show the wide applicability the method is extensively tested with measurement data of various flat-plate collector systems covering different applications (below 200 °Celsius), sizes and orientations. The results show that the method can forecast the solar yield very accurately with a Mean Absolute Range Normalized Error (MARNE) of about 5 % using real weather forecasts as inputs and outperforms common forecasting methods by being nearly twice as accurate.

Demand forecasting methods for spare parts logistics for aviation: a real-world implementation of the Bootstrap method

Abstract One of the critical issues that an airline faces in its day-to-day operations is a correct prognosis of the necessary quantity of spare parts that are continuously fed into unexpected maintenance operations. Indeed, there is a critical need for accurate forecasting methods to predict the demand of these spare parts in order to minimize the so-called Aircraft-On-Ground situations. This paper describes the real-world implementation of the Bootstrap method and the assessment of its performance with actual data from aviation logistics. The analysis reveals that the Bootstrap method, while not the most accurate in every case, should be preferred over other popular methods in spare parts forecasting for aviation, because is more agile and can address adequately all categories of demand. A simple decision support system is then presented to assist airline materials managers in using the bootstrap method. The system is expandable and can potentially incorporate other forecasting method as well.

A bottom-up weather-sensitive residential demand model for developing countries. A case study of Abuja, Nigeria

The frequency of power outages being experienced in Sub-Saharan Africa mean that traditional methods of electricity demand forecasting which rely on directly observed demand data are inadequate for use in projections. Nevertheless, accurate forecasting methods are urgently required to ensure efficient power system operations and expansion planning. To address this gap, we develop a novel method to estimate unsuppressed electricity demand for developing countries. This follows a bottom-up approach based on socioeconomic data and a time-use database developed from a householder survey, which are used to generate household profiles using a Markov Chain approach. These profiles are then converted into electrical load time series by a series of appliance models, using reanalysis weather data to accurately represent ambient conditions for the generation of cooling demand profiles. We apply our method to a Nigerian case study, obtaining the first time series of unsuppressed residential electricity demand for the country using the first Time-of-Use Survey (TUS) for Nigerian households. We validate our model outputs using the results of a small-scale residential metering trial, which yielded a correlation coefficient of 0.97, RMSE of 0.04, and percentage error of 6% between measured and model data. This evidences that our method is a credible and practical tool for electrical demand studies in developing countries. Using the model, the forecasted domestic demand for Abuja Electricity Distribution Company ranges between 345 and 575 MW, while that of Nigeria ranges between 3829 and 6605 MW. • A time series model of domestic energy driven by household activity enables the assessment of climate scenarios on demand • The first Time-of-Use Survey for Nigeria, enabling representative stochastic modelling of individual and aggregate households • It includes a socioeconomic model which can be extended to vary the electrical appliance suite for energy efficiency studies • Model provides a means of cross validating simpler methods and examining the credibility of limited demand measurement data • It facilitates use of time series operation and planning approaches that are standard in Western nations.

Short-Time Wind Speed Forecast Using Artificial Learning-Based Algorithms.

The need for an efficient power source for operating the modern industry has been rapidly increasing in the past years. Therefore, the latest renewable power sources are difficult to be predicted. The generated power is highly dependent on fluctuated factors (such as wind bearing, pressure, wind speed, and humidity of surrounding atmosphere). Thus, accurate forecasting methods are of paramount importance to be developed and employed in practice. In this paper, a case study of a wind harvesting farm is investigated in terms of wind speed collected data. For data like the wind speed that are hard to be predicted, a well built and tested forecasting algorithm must be provided. To accomplish this goal, four neural network-based algorithms: artificial neural network (ANN), convolutional neural network (CNN), long short-term memory (LSTM), and a hybrid model convolutional LSTM (ConvLSTM) that combines LSTM with CNN, and one support vector machine (SVM) model are investigated, evaluated, and compared using different statistical and time indicators to assure that the final model meets the goal that is built for. Results show that even though SVM delivered the most accurate predictions, ConvLSTM was chosen due to its less computational efforts as well as high prediction accuracy.

Impact of building greening on building energy consumption: A quantitative computational approach

Abstract As China’s urbanization process continues to accelerate, building energy consumption is becoming an increasingly serious issue, and more attention is being focused on the use of sustainable green buildings to control building energy consumption. The aim of this study is to establish a method to quantitatively predict the impact of greening on building energy consumption. First, we used NASA satellite data to establish the Beijing Land Characteristics Index, including land surface temperature (LST), normalized differential vegetation index (NDVI), building silhouette (BS), impervious surfaces index (ISI), and modified normalized difference water index (MNDWI). Then, we used the spatial autoregressive model to explore the relationship between building greening and outdoor temperature and finally used the degree-day method to establish the connection between building greening and building energy consumption. The regression results show that the outdoor temperature of the building has a high spatial autocorrelation. For every NDVI increase of 0.1, the outdoor temperature is reduced by 1.317 °C. Using the final model to calculate the Shuangjing commercial district data, for every NDVI increase of 0.1 in the Shuangjing area (116.4656241, 39.89360353), the building energy consumption decreased by 7.771%. The results of this study provide strong support for urban greening policy implementation and a more accurate quantitative forecasting method for greening investment and building energy consumption to meet the general cleaner production trend in building construction.

Short Term Electric Load Forecasting of Kathmandu Valley of Nepal using Artificial Neural Network

Decision making in the energy sector has to be based on accurate forecasts of the load demand. Short-term forecasting, which forms the focus of this paper, gives a day ahead hourly forecast of electric load. This forecast can help to make important decisions in the field of scheduling, contingency analysis, load flow analysis, preventing imbalance in the power generation and load demand, load switching strategies, thus leading to greater network reliability and power quality. A method called Artificial Neural Network is used to anticipate the future load of Kathmandu Valley of Nepal. The Neural Network is build, trained with historical data along with seven different input variables and used for prediction of day ahead 24 hours load. The output is validated with the real Load collected from NEA. In addition, forecasting is performed by some other time series methods as well, and whose output are compared with that of neural network. The range of Mean Absolute Deviation for four different time series models lied between 1.50-2.59. When the errors were calculated in terms of MSE and MAPE the range of these values were found to be in between 2.59-7.78, and 1.61- 5.07 respectively. The Artificial Neural Network proved to be the more accurate forecast method when the results are compared in terms of error measurements with a MAD having 1.23, MSE having 1.79 and MAPE having 1.17. The Neural Network proved to be more accurate method comparatively with satisfactory minimum error.

Forecasting of Unemployment Rate in Malaysia Using Exponential Smoothing Methods

One of the issues that triggers worlds lately is the increasing rate of the unemployment rate. Consequently, this research objective is to compare the most accurate forecast method and to find the most suitable period to predict the future of Malaysia’s unemployment rate in 2016. There are five sets of Malaysia’s unemployment rate and three forecasting methods being used which are Naïve, Simple Exponential Smoothing (SES) and Holt’s method. The forecasting model was then selected based on the smallest accuracy measures. The results indicated that Holt’s is the optimal model in forecasting the overall yearly unemployment rate, male yearly unemployment rate and overall quarterly unemployment rate. Furthermore, for female yearly unemployment rate and overall monthly unemployment rate, the best forecasting method was SES. Meanwhile, the overall unemployment rate of Malaysia in year 2016 was predicted to be 2.9% while 3.4% was estimated to be the value of unemployment rate for second half year of 2016 by using quarterly and monthly data. The forecast value was remained the same as previous year for overall yearly male data and female data which were 2.9% and 3.3% respectively. Lastly, the best period in forecasting Malaysia’s overall unemployment rate was found to be month with the value of 3.4%.

A new forecasting method for univariate time series

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Wavelet Neural Network Based Multiobjective Interval Prediction for Short-Term Wind Speed

As a source of clean and pollution-free renewable energy, wind power has attracted much attention and has been increasingly integrated into the existing power system. However, the uncertain and volatile wind speed makes the utilization of wind power a challenging task. Hence, it is essential to design an accurate forecast method to deal with the uncertainty caused by wind speed. This paper proposes a multiobjective interval prediction method based on wavelet neural network (WNN) for short-term wind speed forecast. This method can generate a set of Pareto optimal solutions which represents a set of prediction models that can directly construct the prediction intervals. An advanced multiobjective evolutionary algorithm, preference inspired co-evolutionary algorithm using goal vectors, is investigated to train the WNN model. Two case studies are carried out with real wind speed data of Victoria and Edmonton in Canada to justify the effectiveness of the proposed method. The numerical results also show the superiority of the proposed forecast approach compared with some benchmark methods.