Forecast Engine Research Articles

Modernising Supply Chain Analytics with Demand Forecasting Engine

In todays’ dynamic market landscape accurate demand forecasting plays a role, in optimizing supply chain operations reducing inventory expenses and improving customer satisfaction. The article is a case study of one of the leading automotive spare parts manufacturers with thousands of products. The main aim of the study is to forecast the demand of the factory on bi-weekly basis and monthly basis, compare the performance metrics with different forecast approaches and suggest suitable model.

Management Information Systems for Tree Fruit–2: Design of a Mango Harvest Forecast Engine

Spatially enabled yield forecasting is a key component of farm Management Information Systems (MISs) for broadacre grain production, enabling management decisions such as variable rate fertilization. However, such a capability has been lacking for soft (fleshy)-tree-fruit harvest load, with relevant tools for automated assessment having been developed only recently. Such tools include improved estimates of the heat units required for fruit maturation and in-field machine vision for flower and fruit count and fruit sizing. Feedback on the need for and issues in forecasting were documented. A mango ‘harvest forecast engine’ was designed for the forecasting of harvest timing and fruit load, to aid harvest management. Inputs include 15 min interval temperature data per orchard block, weekly manual or machine-vision-derived estimates of flowering, and preharvest manual or machine-vision-derived estimates of fruit load on an orchard block level across the farm. Outputs include predicted optimal harvest time and fruit load, on a per block and per week basis, to inform harvest scheduling. Use cases are provided, including forecast of the order of harvest of blocks within the orchard, management of harvest windows to match harvesting resources such as staff availability, and within block spatial allocation of resources, such as adequate placement of harvest field bin and frost fans. Design requirements for an effective harvest MIS software artefact incorporating the forecast engine are documented, including an integrated database supporting spatial query, data analysis, processing and mapping, an integrated geospatial database for managing of large spatial–temporal datasets, and use of dynamic web map services to enable rapid visualization of large datasets.

Forecasting energy spot prices: A multiscale clustering recognition approach

Effective and timely forecasting of energy spot price plays a key role in the international bulk commodities market, which is related to the goal and direction of the reform of pricing mechanism. Previous studies are based on the decomposition-ensemble model to forecast the time series of high volatility in energy spot price, but ignore the adaptability of different models for different sub-series. This paper presents a hybrid forecasting model based on multiscale clustering recognition approach. Data preprocessing module is used to split the original series into several sub-series to reduce the complexity of the data, and then the optimal mode of input data is determined by feature selection. To identify the characteristic of sub-series, fuzzy cluster approach is introduced to distinguish with their complexity, which can divide adaptively them into different components with different frequency. This divide and conquer strategy give the reference for selecting suitable forecasting engine. With the help of tuned forecasting engines, these components are conducted respective prediction and the final results are reconfigured with the existing time point. The proposed model takes fully the advantages of different models into account, which ensures to use the data effectively. Through the empirical results, it demonstrates that the multiscale clustering recognition approach can both produce satisfactory prediction which is close to the actual values and be regarded as a promising tool for forecasting energy spot price.

Intelligent forecast engine for short-term wind speed prediction based on stacked long short-term memory

This paper presents machine learning techniques for short-term wind speed prediction by exploiting their computational intelligence capabilities such as random forest regression (RFR), support vector regression (SVR), radial basis function neural networks (RBFNN), and long short-term memory (LSTM) on various wind farm datasets located in Pakistan. Initially, predictions are obtained by employing baseline regressors, RFR and RBFNN in terms of error indices. Later, a stacked LSTM forecast engine (SLFE) has been proposed to improve the efficacy, accuracy, and prediction capability of the forecast engine by using leaky rectified linear units (Leaky ReLU) as kernel function. SLFE has been implemented and tested on the datasets acquired from four different wind farms having a temporal locality of 10 min for short-term wind speed prediction. Furthermore, an ensemble of stacked LSTM with RFR, SVR, and RBFNN has also been developed for the comparison. The strength of the SLFE has been evaluated in terms of various performance measures such as mean absolute error (MAE), root mean squared error (RMSE), R2Score, and explained variance score (EVS). The efficacy of the proposed models is evaluated in terms of performance metrics, MAE, RMSE, R2Score and EVS, 0.043, 0.065, 0.813, and 0.814, respectively, demonstrating the worth of the forecast engine. Additionally, statistical one-way ANOVA is also carried out with multiple independent executions of the proposed algorithm to demonstrate the robustness, efficiency, and reliability of the model.

A novel integrated price and load forecasting method in smart grid environment based on multi-level structure

Prediction of load and price are two critical key in power system planning and operation. Most of the recent works in this area forecast the load and price signals separately but, a dynamic model in smart grid is evaluated while, the customers may have opportunity to react the proposed prices changing through shifting the electricity usages from expensive to cheaper hours. So, the load and price signals are coupled strongly which made the previous prediction models ineffective. In this research, a synthetic prediction approach has been proposed by considering the load and price signals, simultaneously. This method works as multi-input multi-output (MIMO) model based on least square support vector machine (LSSVM) forecast engine. Furthermore, a dyadic wavelet transform (DWT) is suggested in this approach to decompose the original signal into different small sub-signals. Beside of that, the modified mutual information (MMI) filter has been used to choose the best candidate input of forecast engine. The learning section is also coupled with novel modified optimization algorithm based on gravitational search algorithm (GSA) which called as modified GSA (MGSA). Finally, various forecasting errors have been considered as average mean absolute percentage error and error variance to get the comparison outcomes and performance of forecasting approaches. For this purpose, different markets have been considered as test case to show the efficiency of suggested approach.

RETRACTED: Combination of cuckoo search and wavelet neural network for midterm building energy forecast

The electrical load prediction for buildings plays a critical role in the smart-grid paradigm, since accurate predictions provide efficient energy management. A synthetic approach has been used in two buildings as the case studies with wavelet neural network (WNN) as a preparation for a process from the signal assessment perspective to forecast the density of electricity requirements. In this paper, singular spectrum analysis (SSA) and WNN based forecast engine have been considered. In this model, the free parameters of WNN are tuned optimally by cuckoo search (CS) algorithm. Using parameters of ten-dimensional variables of 29 weekdays as learning samples, this technique has been performed in a hotel and a mall, where the used electricity pattern respectively denoted dynamic and static series. By comparing the proposed approach with other models, it can be claim that WNN can generally enhance the forecasting precision for the hotel, although it is not essential for the mall. Particularly, the analogous stable amount that is about 0.65 W/m2of absolute error was gotten for the mall and the hotel buildings, where ε was less than 0.1. Simultaneously, the stable quantitative magnitudes of relative errors were around 4% and 6% for the mall and the hotel, respectively. Using the brief historical measurement, real-time forecasting approach of interim electricity requirement is proposed which can be applied to have smarter energy control.

A New Combinatory Approach for Wind Power Forecasting

Wind power generation is considerably dependent on the weather condition and it is correlated closely with the air density, wind speed, and its direction through considering the transformer tap change cost. Therefore, an accurate prediction model is required to forecast and adopt with the complicated signals. In this article, an accurate prediction method trained with improved wavelet transform (IWT) to decompose the original signal to subsignals, new feature selection based on the maximum dependence, maximum relevancy, and minimum redundancy to filter the signal and select the best candidate inputs and a synthetic forecasting engine with minimum forecasting error is proposed. The proposed method of forecasting engine composed two-dimensional convolution neural network (TDCNN) and trained by improved optimization algorithm based on particle swarm optimization. The proposed improved optimization algorithm will fine-tune the weights of TDCNN to increase the prediction accuracy of the forecast engine. The training optimization processes and effectual fast classification are the main duty of the proposed intelligent algorithm. The efficiency of the suggested prediction approach is widely evaluated and compared to other prediction approaches using the practical power market data. A simulation study is conducted over the trained model and the results are discussed in detail. Obtained numerical results and also analysis in short-term and long-term forecasting horizons validate the high performance and advantages of the introduced approach.

A novel joint bidding technique for fuel cell wind turbine photovoltaic storage unit and demand response considering prediction models analysis Effect's

The stake of distributed generation resources like fuel cell in daily market is proved to be a major uncertain problem. The volatile character of market price together with the unbalanced nature of power can take hold of economic advancement of distributed generation resources which in turn can culminate in diversion retribution while the market is being struck. This study introduces a market participation model in share conditions to improve the profit for Fuel Cell/wind turbine/storage/photovoltaic and demand response. To solve the mentioned problem, an accurate prediction model is presented in this paper. This model is based on complete ensemble empirical mode decomposition, and multiple artificial neural network which is coupled with Broyden water cycle algorithm. By this algorithm, the prediction accuracy of proposed forecast engine is enhanced and could get the better results. A sure-footed stochastic optimization approach was deployed in order to take prices of markets and distributed generation resources into account. In the generation of distributed generation resources, forecasting error database in everyday, modified, and depressed market was drawn on to induce probabilistic scenario. Improbable variables were discarded by a neuro-fuzzy model. Eventually, to illustrate the joint model strategy suggested in the study, a testing system contains fuel cell/wind turbine/storage unit/photovoltaic and demand response was utilized and the attained results were calculated in two different periods.

An Innovative Optimization Strategy for Efficient Energy Management With Day-Ahead Demand Response Signal and Energy Consumption Forecasting in Smart Grid Using Artificial Neural Network

In this study, a novel framework is proposed for efficient energy management of residential buildings to reduce the electricity bill, alleviate peak-to-average ratio (PAR), and acquire the desired trade-off between the electricity bill and user-discomfort in the smart grid. The proposed framework is an integrated framework of artificial neural network (ANN) based forecast engine and our proposed day-ahead grey wolf modified enhanced differential evolution algorithm (DA-GmEDE) based home energy management controller (HEMC). The forecast engine forecasts price-based demand response (DR) signal and energy consumption patterns and HEMC schedules smart home appliances under the forecasted pricing signal and energy consumption pattern for efficient energy management. The proposed DA-GmEDE based strategy is compared with two benchmark strategies: day-ahead genetic algorithm (DA-GA) based strategy, and day-ahead game-theory (DA-game-theoretic) based strategy for performance validation. Moreover, extensive simulations are conducted to test the effectiveness and productiveness of the proposed DA-GmEDE based strategy for efficient energy management. The results and discussion illustrate that the proposed DA-GmEDE strategy outperforms the benchmark strategies by 33.3% in terms of efficient energy management.

An Intelligent Integrated Approach for Efficient Demand Side Management With Forecaster and Advanced Metering Infrastructure Frameworks in Smart Grid

The development of advanced metering infrastructure (AMI) in smart grid (SG) had enabled consumers to participate in demand-side management (DSM) using the price-based demand response (DR) programs offered by the distribution companies (DISCO). This way, not only the consumers minimize their electricity bills and discomfort, but also the DISCOs can handle peak power demand and reduce the carbon (CO 2 ) emissions in a controlled manner. Building an optimization framework that will minimize cost, peak demand, waiting time, and CO 2 emission is not only a challenging task but also a concern of DSM. Most analyses are based on cost and peak-to-average ratio (PAR) minimization, but the effectiveness of the DSM framework is equally determined by user comfort and CO 2 emission. Considering only one objective (cost) or two objectives (cost and PAR) is not sufficient. Thus, for DSM framework to achieve these four relatively independent objectives at the same time, minimized cost, PAR, CO 2 emission, and user discomfort, an energy management controller (EMC) based on our proposed algorithm hybrid bacterial foraging and particle swarm optimization (HBFPSO) is employed that return optimal power usage schedule for consumers. A novel DSM framework consists of four units: (i) DISCO, (ii) multi-layer perceptron (MLP) based forecast engine, (iii) AMI, and (iv) demand-side energy management modules is successfully developed in this work. To validate the proposed model, extensive simulations are conducted and results are compared with the benchmark models like genetic algorithm (GA), bacterial foraging optimization algorithm (BFOA), binary particle swarm optimization (BPSO), and a hybrid combination of genetic and binary particle swarm optimization (GBPSO) in terms of electricity cost, PAR, user comfort, and CO 2 emissions. The simulation results demonstrate effectiveness of our proposed model to outperform all the benchmark models in optimizing the consumer and DISCO objectives. The proposed scheme has reduced electricity cost, user discomfort, PAR, and CO 2 emission for the residential sector by 15.14%, 4.6%, 61.6%, and 52.86% in scenario 1, 62.60%, 4.56%, 60.77%, and 27.77% in scenario 2, and 26.03%, 4.54%, 63.78%, and 23.02% in scenario 3, as compared to without an EMC. Similarly, for commercial sector the proposed HBFPSO algorithm reduces electricity cost, user discomfort, PAR, and CO 2 emission by 11.31%, 5.5%, 60.9%, and 38.18% in scenario 1, 64.9%, 5.56%, 44.08%, and 58.8% in scenario 2, 15.31%, 5.26%, 78.22%, and 15.58% in scenario 3. Likewise, the proposed algorithm also has superior performance for the industrial sector for all the three scenarios.

Outlier-robust hybrid electricity price forecasting model for electricity market management

Electricity market management is of great importance for cleaner production in the development of society. However, despite this significance, electricity price forecasting remains a challenging task. Hybrid models are widely employed for forecasting electricity price, which has the characteristics of being non-stationarity, random, and non-linear. Despite their success, current hybrid models require improvement. In particular, data preprocessing, artificial intelligence optimization, feature selection, and basic forecasting engine selection should be considered. In this study, in addition to these issues, we consider the negative influence of outliers on the modeling of electricity price. In particular, a novel outlier-robust hybrid model is developed for forecasting electricity price, which combines a basic forecasting engine called outlier-robust extreme learning machine model and three new algorithms. Specifically, a new optimizer called chaotic sine cosine algorithm is developed to obtain the ideal parameters for phase space reconstruction, and then a novel feature selection method is developed to construct the optimal features in the modeling of electricity price. Moreover, an effective data preprocessing method is proposed for effective forecasting by capturing electricity price features. Subsequently, experiments based on electricity price data from the electricity markets of Australia and Singapore demonstrate that the proposed model is superior to other benchmark models. Further, the model can be a reliable forecasting method not only in electricity market management, but also in modeling time series with complex nonlinear characteristics and outliers.

Day-Ahead Prediction of Microgrid Electricity Demand Using a Hybrid Artificial Intelligence Model

Improved-performance day-ahead electricity demand forecast is important to deliver necessary information for right decision of energy management of microgrids. It supports microgrid operators and stakeholders to have better decisions on microgrid flexibility, stability and control. The available conventional forecasting methods for electricity demand at national or regional level are not effective for electricity demand forecasting in microgrids. This is due to the fact that the electricity consumption in microgrids is many times less than the regional or national demands and it is highly volatile. In this paper, an integrated Artificial Intelligence (AI) based approach consisting of Wavelet Transform (WT), Simulated Annealing (SA) and Feedforward Artificial Neural Network (FFANN) is devised for day-ahead prediction of electric power consumption in microgrids. The FFANN is the basic forecasting engine of the proposed model. The WT is utilized to extract relevant features of the target variable (electric load data series) to obtain a cluster of enhanced-feature subseries. The extracted subseries of the past values of the electric load demand data are employed as the target variables to model the FFANN. The SA optimization technique is employed to obtain the optimal values of the FFANN weight parameters during the training process. Historical information of actual electricity consumption, meteorological variables, daily variations, weekly variations, and working/non-working day indicators have been employed to develop the forecasting tool of the devised integrated AI based approach. The approach is validated using electricity demand data of an operational microgrid in Beijing, China. The prediction results are presented for future testing days with one-hour time interval. The validation results demonstrated that the devised approach is capable to forecast the microgrid electricity demand with acceptably small error and reasonably short computation time. Moreover, the prediction performance of the devised approach has been evaluated relative to other four approaches and resulted in better prediction accuracy.

Application of hybrid forecast engine based intelligent algorithm and feature selection for wind signal prediction

This paper presents a new prediction model based on empirical mode decomposition, feature selection and hybrid forecast engine. The whole structure of proposed model is based on nonstationarity and non-convex nature of wind power signal. The hybrid forecast engine consists of three main stages as; empirical mode decomposition, an intelligent algorithm and back propagation neural network. All parameters of proposed neural network will be optimized by intelligent algorithm. Effectiveness of the proposed model is tested with real-world hourly data of wind farms in Spain and Texas. In order to demonstrate the validity of the proposed model, it is compared with several other wind speed and power forecast techniques. Obtained results confirm the validity of the developed approach.

An improved wavelet transform and multi-block forecast engine based on a novel training mechanism

In this paper, a novel prediction technique is proposed to predict wind-power generation. Because of the growth of wind-generated electricity as a component of power grids, various wind-power prediction methods have been proposed recently by researchers. To achieve accurate prediction, a novel approach using a dual-tree complex wavelet transform, a new feature selection procedure, and a combinatorial prediction engine has been implemented to forecast wind-power generation. To improve feature selection to reduce diagnostic efficiency degradation caused by outliers in data-driven diagnostics, an outlier-insensitive combinatorial feature selection procedure has been used to determine candidate subgroup characteristics. Furthermore, a multi-stage forecast engine equipped with a new training mechanism for optimizing free parameters and based on the Elman neural network (ENN) is presented in this work. This training mechanism was developed using an efficient stochastic search method to attain the high learning capabilities of the proposed ENN-based forecast engine. The proposed model has been applied to real-world engineering data from Alberta, Canada, and Oklahoma, United States. The outcomes achieved by the different forecasting methods are compared, proving the effectiveness of the proposed procedure.

A dynamic evaluation framework for ambient air pollution monitoring

• A novel dynamic evaluation system is proposed for monitoring urban ambient air condition. • The enhanced sine cosine algorithm is proposed to tune the parameters of forecast engine. • The fuzzy synthetic evaluation model with entropy weight is used to perform the air quality levels. • Three real data examples are conducted to investigate the proposed monitoring system. Accurate real-time prediction of urban air quality is one of the most important problems in control and improve ambient air condition globally. Therefore, the modeling and applications of air pollutant forecasting and evaluation has attracted the attention of researchers in recent years. Based on the method of fuzzy mathematical synthetic evaluation, this paper built a dynamic evaluation model for the purpose of mastering the future air quality immediately. A newly proposed computational intelligence optimization algorithm is improved to optimize the least square support vector machine, which can generate rolling forecasts of six air pollutants concentration. The information of future air quality status is built by the fuzzy synthetic assessment model based on entropy weighing method. The results and analysis of air quality monitoring show that accurate and reliable forecast of urban air pollutants concentration are possible and the air quality conditions can be evaluated objectively. Through the simulation design, it proves that the proposed dynamic evaluation model can provide a practical tool for ambient air ambient quality evaluation.

Different states of multi-block based forecast engine for price and load prediction

This work proposes different prediction models based on multi-block forecast engine for load and price forecast in electricity market. Due to high correlation of load and price signals, the density of this reaction can affect the demand curve and shift it in market. Furthermore, to improve the operation and planning improvement in the power system, an accurate prediction model can play an important role. So, in this paper, a complex prediction approach is presented based on feature selection, and multi-stage forecast engine. The forecast engine is comprised of multi-block neural network (NN) and optimized by an intelligent algorithm to increase the training mechanism and forecasting abilities. Moreover, different models of multi-block forecast engine are presented in this paper to choose the effective model. In other words, different combinations of NN are tested in the same prediction condition to show their abilities. The proposed model is tested over real-world engineering test cases through comparison with other prediction methods. Obtained results demonstrate the validity of the proposed model.

Two stage forecast engine with feature selection technique and improved meta-heuristic algorithm for electricity load forecasting

Short-term load forecasting is of major interest for the restructured environment of the electricity market. Accurate load forecasting is essential for effective power system operation, but electricity load is non-linear with a high level of volatility. Predicting such complex signals requires suitable prediction tools. This paper proposes a hybrid forecast strategy including novel feature selection technique, and a complex forecast engine based on a new intelligent algorithm. The electricity load signal is first filtered by feature selection technique to select appropriate candidates as input for the forecast engine. Then, the proposed two stage forecast engine is implemented based on ridgelet and Elman neural networks. All forecast engine parameters are chosen based on a novel intelligent algorithm to improve its accuracy and capability. Different electricity markets were considered as test cases to compare the proposed method with several current algorithms. Additionally, the proposed forecasting model measures the absolute forecasting errors in this work (among seven types of measurements i.e., absolute forecasting errors, measures based on percentage errors, symmetric errors, measures based on relative errors, scaled errors, relative measures and other error measures). The results validate the effectiveness of the proposed method.

A new solar power output prediction based on hybrid forecast engine and decomposition model

Regarding to the growing trend of photovoltaic (PV) energy as a clean energy source in electrical networks and its uncertain nature, PV energy prediction has been proposed by researchers in recent decades. This problem is directly effects on operation in power network while, due to high volatility of this signal, an accurate prediction model is demanded. A new prediction model based on Hilbert Huang transform (HHT) and integration of improved empirical mode decomposition (IEMD) with feature selection and forecast engine is presented in this paper. The proposed approach is divided into three main sections. In the first section, the signal is decomposed by the proposed IEMD as an accurate decomposition tool. To increase the accuracy of the proposed method, a new interpolation method has been used instead of cubic spline curve (CSC) fitting in EMD. Then the obtained output is entered into the new feature selection procedure to choose the best candidate inputs. Finally, the signal is predicted by a hybrid forecast engine composed of support vector regression (SVR) based on an intelligent algorithm. The effectiveness of the proposed approach has been verified over a number of real-world engineering test cases in comparison with other well-known models. The obtained results prove the validity of the proposed method.

Novel forecasting model based on improved wavelet transform, informative feature selection, and hybrid support vector machine on wind power forecasting

Wind speed/power prediction plays an important role in large-scale wind power penetration because of the wind volatility and uncertainty. In this paper, an accurate forecast model is presented based on improved wavelet transform, informative feature selection and hybrid forecast engine. The proposed forecasting engine is based on support vector machine which is an appropriate prediction forecast engine due to its ability to discover natural structures of wind speed/power variation. The mentioned forecast engine is equipped with an intelligent algorithm and enhances its prediction accuracy. For this purpose, we applied a new version of enhanced particle swarm optimization in this work as the optimization algorithm. Effectiveness of the proposed forecast model is extensively evaluated by real-world electricity market through comparison with well-known forecasting methods. Obtained numerical results and analysis demonstrate the validity and superiority of the proposed method.

A novel hybrid prediction model for aggregated loads of buildings by considering the electric vehicles

In this paper, a new prediction model for aggregated loads of buildings has been propose. Due to high correlation of prediction performance with related horizons and aggregated more customers, a new strategy is developed to provide a forecasting model based on high accuracy. While, consumption shape of a single users normally has low structure to be exploited and vice versa. So, combining different users develops the relative prediction performance up to special point. Outside this point, no more enhancement in relative performance can be found. This model is useful in optimal power system operation and planning in micro-grids. In this paper, besides the aggregated loads of building, the electric vehicle (EV) impact on network has been considered. While, by considering the load growth prediction and impact of EV adaption on load curves this problem can be an important issue for the power grid operation and management. So, an accurate prediction model is presented in this work which is composed of new feature selection and enhanced support vector machine (ESVM) based forecast engine. Effectiveness of the proposed model is carried out to the part of Budapest city through comparing with other prediction models. Obtained results demonstrate the validity of proposed method.