Fuzzy Inference System in Energy Demand Prediction

No abstract available

Fuzzy Interference System in Energy Demand Prediction

No abstract available

DEVELOPMENT OF FORECASTING AND SCHEDULING METHODS AND DATA ANALYTICS BASED CONTROLS FOR SMART LOADS IN RESIDENTIAL BUILDINGS.

DEVELOPMENT OF FORECASTING AND SCHEDULING METHODS AND DATA ANALYTICS BASED CONTROLS FOR SMART LOADS IN RESIDENTIAL BUILDINGS

Various multistage ensembles for prediction of heating energy consumption

Feedforward neural network models are created for prediction of daily heating energy consumption of a NTNU university campus Gloshaugen using actual measured data for training and testing. Improvement of prediction accuracy is proposed by using neural network ensemble. Previously trained feed-forward neural networks are first separated into clusters, using k-means algorithm, and then the best network of each cluster is chosen as member of an ensemble. Two conventional averaging methods for obtaining ensemble output are applied; simple and weighted. In order to achieve better prediction results, multistage ensemble is investigated. As second level, adaptive neuro-fuzzy inference system with various clustering and membership functions are used to aggregate the selected ensemble members. Feedforward neural network in second stage is also analyzed. It is shown that using ensemble of neural networks can predict heating energy consumption with better accuracy than the best trained single neural network, while the best results are achieved with multistage ensemble

Fault Prediction Based on Leakage Current in Contaminated Insulators Using Enhanced Time Series Forecasting Models

To improve the monitoring of the electrical power grid, it is necessary to evaluate the influence of contamination in relation to leakage current and its progression to a disruptive discharge. In this paper, insulators were tested in a saline chamber to simulate the increase of salt contamination on their surface. From the time series forecasting of the leakage current, it is possible to evaluate the development of the fault before a flashover occurs. In this paper, for a complete evaluation, the long short-term memory (LSTM), group method of data handling (GMDH), adaptive neuro-fuzzy inference system (ANFIS), bootstrap aggregation (bagging), sequential learning (boosting), random subspace, and stacked generalization (stacking) ensemble learning models are analyzed. From the results of the best structure of the models, the hyperparameters are evaluated and the wavelet transform is used to obtain an enhanced model. The contribution of this paper is related to the improvement of well-established models using the wavelet transform, thus obtaining hybrid models that can be used for several applications. The results showed that using the wavelet transform leads to an improvement in all the used models, especially the wavelet ANFIS model, which had a mean RMSE of 1.58 × 10−3, being the model that had the best result. Furthermore, the results for the standard deviation were 2.18 × 10−19, showing that the model is stable and robust for the application under study. Future work can be performed using other components of the distribution power grid susceptible to contamination because they are installed outdoors.N/

Performance measurement, forecasting and optimization models for construction projects

Performance evaluation facilitates tracking and controlling project progress. Project control consists of two main steps: measurement and decision-making. In the measurement step, key performance indicators (KPIs) are designed to evaluate a project’s different aspects and are used as a thermometer to determine the health status of the project. In the decision-making step project performance is forecasted and analyzed to support needed management actions. While considerable work is available on the quantitative performance of projects, less attention is directed to qualitative performance. This research presents a framework for qualitative measurement, prediction, and optimization of construction project performance to enhance the progress reporting process and to support management in taking corrective actions, if needed. The framework has three newly developed models; KPI prediction model, performance indicator (PI) prediction model and performance optimization model (POM). The framework is developed for performance measurement, prediction, and optimization of construction projects based on six selected KPIs (cost, time, quality, safety, client satisfaction, and project team satisfaction). The selection is based on the results of a questionnaire and the literature review. Qualitative data of KPIs was collected from 119 construction projects and were then utilized in the development of the three models. The first model maps the KPIs of three critical project stages to the whole project KPIs, based on soft computing methods. Three different soft computing techniques are studied for this purpose and their results are compared: the neuro-fuzzy technique, using Fuzzy C-means algorithm (FCM), and subtractive clustering, and artificial neural networks (ANN). The neuro-fuzzy model is developed for predicting the KPIs of the next stages of a project. The second model used the forecasted results of the first model to generate a single composite PI expressing the health status of the project. The relative weight of each KPI used in calculating the project PI is determined using the Analytic Hierarchy Process (AHP) and Genetic Algorithm (GA). Performance Optimization Model (POM) is the third model. It is used for selecting suitable corrective actions considering the project status expressed by the six KPIs stated above. The developed model can be applied in the initial and middle stage of the project to assist owners in the improvement of the overall project PI and in the improvement of individual KPIs. Different possible modes are considered for project activities based on different ways, referee to here as modes, for resource allocation, execution methods, and/or choice of different materials. GA is applied to choose among different activity modes and optimize project performance using POM. The number of activities and their modes are flexible and do not have any limitations. MATLAB software is used for developing the models in this research. The developed framework and its three models are expected to assist owners and their agents in managing their project effectively. Validation was conducted by using the data from 16 real projects to confirm the model’s effectiveness and to compare the results of the soft computing techniques. These results indicate that a neuro-fuzzy technique using subtractive clustering performs better than both the neuro-fuzzy technique with FCM and ANN in predicting project KPIs. The automated framework employs a set of performance indicators to evaluate, predict, and optimize the construction project’s performance, qualitatively. It applies different soft computing techniques and compares their results to choose the best technique. The developed framework can be used in construction projects to help decision-makers evaluate and improve the performance of their projects

Prediction of municipal solid waste generation : an investigation of the effect of clustering techniques and parameters on ANFIS model performance

Abstract: The present waste management system and facilities in most developing countries are insufficient to combat the challenge of increasing rate of solid waste generation. To achieve success in sustainable solid waste management, planning plays a crucial role. Accurate prediction of waste quantities generated will immensely help to overcome the challenge of deficient-planning of sustainable waste management. This challenge has necessitated the need for modelling approach. In modelling the complexity within a system, a paradigm-shift from classical-model to artificial intelligent model has been necessitated. Previous researches which used Adaptive Neuro-Fuzzy Inference System (ANFIS) for waste generation forecast did not investigate the effect of clustering-techniques and parameters on the performance of the model despite its significance in achieving accurate prediction. This study therefore investigates the impact of the parameters of three clustering-technique namely: Fuzzy c-means (FCM), Grid-Partitioning (GP) and Subtractive-Clustering (SC) on the performance of the ANFIS model in predicting waste generation using South Africa as a case study. Socio-economic and demographic provincial-data for the period 2008-2016 were used as input-variables and provincial waste quantities as output-variable. ANFIS model clustered with GP using triangular input membership-function (tri-MF) and a linear type output membership-function (ANFIS-GP1) is the optimal model with Mean Absolute Percentage Error (MAPE), Mean Absolute Deviation (MAD), Root Mean Square Error (RMSE) and Correlation Co-efficient (R2) values of 12.6727, 0.6940, 1.2372 and 0.9392 respectively. Based on the result in this study, ANFIS-GP with a triangular membership-function is recommended for modelling waste generation. The tool presented in this study can be utilized for the national repository of waste generation data by the South Africa Waste Information Centre (SAWIC) in South Africa and it is also applicable to waste-planners in developing countries for reliable and accurate prediction of annual waste generation

Building an ANFIS-based Decision Support System for Regional Growth: The Case of European Regions

This paper proposes a Decision Support System that can provide European policy makers with systematic guidance in allocating and prioritizing scant public resources. We do so by taking the stance of the Smart Specialisation Strategies which aim at consolidating the regional strengths and make effective and efficient use of public investment in R&D. By applying the ANFIS method we were able to understand how – and to what extent – the competitiveness drivers promoted technological development and how the latter contributes to the economic growth of European regions. We used socio-economic, spatial, and patent-based data to train, test and validate the models. What emerges is that an increase of R&D investments enhances the regional employment rate and the number of patents per capita; in turn, by taking into account the several combinations of specialization and diversification indicators, this leads to an increase of the regional GDP

Cuckoo search based adaptive neuro-fuzzy inference system for short-term load forecasting

Short-Term Load Forecasting (STLF) is an essential input for power system operation computations to achieve proper system balancing. General economy and security of power system depend on accurate STLF. The accuracy of forecasting model depends on the number and types of the forecasting variables. Furthermore, a day-ahead hourly-load forecast has to reach the decision makers before the elapse of a pre-set time. Conventional methods used in determining future load demand were not able to explore all the available variables in a particular forecasting region. Moreover, artificial intelligence methods like Adaptive Neuro-Fuzzy Inference System (ANFIS), are associated with computational difficulties, thus influence the speed and accuracy of the model. Therefore, these variables need to be investigated so as to make the forecasting model simple and easy to use. Similarly, the forecasting speed needs to be improved. This thesis presents the development of short-term electric load demand forecasting algorithm, with the aim to improve the forecasting accuracy and speed. It starts with the development of data selection and data processing framework, through the use of correlation analysis, hypothesis test and wavelet transform. Variables of the four seasons in one year were investigated and were classified based on the available weather and historical load data in each season. To reduce the variability in the forecasting data, wavelet transform is used. The whole forecasting algorithm has been developed by integrating Cuckoo Search (CS) algorithm with ANFIS to produce CS-ANFIS model. CS was used to improve the forecasting capability and speed of the traditional ANFIS, by replacing the derivative-based gradient descent optimization algorithm with CS in backward pass. Its purpose is to update the antecedent parameters of the traditional ANFIS, through the determination of an optimal value of the error merging between the ANFIS output and targeted output. The whole system is validated by the comparison with an existing ANFIS model, and two other ANFIS models optimized with Particle Swarm Optimization (PSO-ANFIS) and Genetic Algorithm (GA-ANFIS). The developed CS-ANFIS model proved to be superior to these models in terms of accuracy and forecasting time. A reduction in average mean absolute percentage error of 84.48% for one year forecast is recorded using the developed CS-ANFIS, and 77.32% with the proposed data selection approach. The model was found to forecast the future load demand within an average period of 37 seconds, as compared to the traditional ANFIS which recorded an average forecasting time of 219 seconds. It can therefore, be accepted as a tool for forecasting future energy demand at utility level to improve the reliability and economic operation of the utility