"A new linguistic out-sample approach of fuzzy time series for daily forecasting of Malaysian electricity load demand"

The fuzzy logical relationships and the midpoints of interval have been used to determine the numerical in-out-samples forecast in the fuzzy time series modeling. However, the absolute percentage error is still yet significantly improved. This can be done where the linguistics time series values should be forecasted inthe beginning before thenumericalforecasted values obtained. This paper introduces thenew approach in determining the linguistic out-sample forecast by using the index numbers of linguistics approach. Moreover, the weights of fuzzy logical relationships are also suggested to compensate the presence of bias in the forecasting. The daily load data from National Electricity Board (TNB) of Malaysia is used as an empirical study and the reliability of the proposed approach is compared with the approach proposed by Yu. The result indicates that the mean absolute percentage error (MAPE) of the proposed approach is smaller than that as proposed by Yu. By using this approach the linguistics time series forecasting and the numerical time series forecasting can be resolved Keywords: Fuzzy time series Index number Weight Electricity load demand Linguistic time series Out-sample forecas

Forecasting peak load electricity demand using statistics and rule based approach

Problem statement: Forecasting of electricity load demand is an essential activity and an important function in power system planning and development. It is a prerequisite to power system expansion planning as the world of electricity is dominated by substantial lead times between decision making and its implementation. The importance of demand forecasting needs to be emphasized at all level as the consequences of under or over forecasting the demand are serious and will affect all stakeholders in the electricity supply industry. Approach: If under estimated, the result is serious since plant installation cannot easily be advanced, this will affect the economy, business, loss of time and image. If over estimated, the financial penalty for excess capacity (i.e., over-estimated and wasting of resources). Therefore this study aimed to develop new forecasting model for forecasting electricity load demand which will minimize the error of forecasting. In this study, we explored the development of rule-based method for forecasting electricity peak load demand. The rule-based system synergized human reasoning style of fuzzy systems through the use of set of rules consisting of IF-THEN approximators with the learning and connectionist structure. Prior to the implementation of rule-based models, SARIMAT model and Regression time series were used. Results: Modification of the basic regression model and modeled it using Box-Jenkins auto regressive error had produced a satisfactory and adequate model with 2.41% forecasting error. With rule-based based forecasting, one can apply forecaster expertise and domain knowledge that is appropriate to the conditions of time series. Conclusion: This study showed a significant improvement in forecast accuracy when compared with the traditional time series model. Good domain knowledge of the experts had contributed to the increase in forecast accuracy. In general, the improvement will depend on the conditions of the data, the knowledge development and validation. The rule-based forecasting procedure offered many promises and we hoped this study can become a starting point for further research in this field

Prediction of Malaysian–Indonesian Oil Production and Consumption Using Fuzzy Time Series Model

Fuzzy time series has been implemented for data prediction in the various sectors, such as education, finance-economic, energy, traffic accident, others. Moreover, many proposed models have been presented to improve the forecasting accuracy. However, the interval-length adjustment and the out-sample forecast procedure are still issues in fuzzy time series forecasting, where both issues are yet clearly investigated in the pre�vious studies. In this paper, a new adjustment of the interval-length and the partition number of the data set is proposed. Additionally, the determining of the out-sample forecast is also discussed. The yearly oil production (OP) and oil consumption (OC) of Malaysia and Indonesia from 1965 to 2012 are examined to evaluate the performance of fuzzy time series and the probabilistic time series models. The result indicates that the fuzzy time series model is better than the probabilistic models, such as regression time series, exponential smoothing in terms of the forecasting accuracy. This paper thus highlights the effect of the proposed interval length in reducing the forecasting error sig�nificantly, as well as the main differences between the fuzzy and probabilistic time series models. Keywords: Fuzzy time series; index of linguistic; oil production–consumption; interval�length; forecasting accurac

Electricity Peak Load Demand using De-noising Wavelet Transform integrated with Neural Network Methods

One of most important elements in electric power system planning is load forecasts. So, in this paper proposes the load demand forecasts using de-noising wavelet transform (DNWT) integrated with neural network (NN) methods. This research, the case study uses peak load demand of Thailand (Electricity Generating Authority of Thailand: EGAT). The data of demand will be analyzed with many influencing variables for selecting and classifying factors. In the research, the de-noising wavelet transform uses for decomposing the peak load signal into 2 components these are detail and trend components. The forecasting method using the neural network algorithm is used. The work results are shown a good performance of the model proposed. The result may be taken to the one of decision in the power systems operation

Fuzzy approach performance of shortterm electricity load forecasting in Malaysia

Many activities (such as economic, education and etc.) 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.Many techniques have been employed in forecasting short term electricity load.They can be classifies either by statistical or artificial intelligent (AI) or hybrid of those two techniques; Statistical techniques and AI techniques. Electricity load demand is influenced by many factors, such as weather, economic, social activities and etc.The relation between load demand and the independent variables is complex and it is not always possible to fit the load curve using statistical models.The complexity and uncertainties of this problem appear suitable for fuzzy methodologies.Hence, the Fuzzy approach was used to forecast electricity load demand.Previous findings showed festive celebration has effect on shortterm electricity load forecasting.Being a multi culture country Malaysia has many major festive celebrations (EidulFitri, Chinese New Year, Deepavali and etc.) but they are moving holidays due to non-fixed dates on the Gregorian calendar.Therefore, the performance of fuzzy approach in forecasting electricity loads when considering the presence of moving holidays was studied.Autoregressive Distributed Lag (ARDL) 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

Estimation of confidence-interval for yearly electricity load consumption based on fuzzy random auto-regression model

Many models have been implemented in the energy sectors, especially in the electricity load consumption ranging from the statistical to the artificial intelligence models. However, most of these models do not consider the factors of uncertainty, the randomness and the probability of the time series data into the forecasting model. These factors give impact to the estimated model’s coefficients and also the forecasting accuracy. In this paper, the fuzzy random auto-regression model is suggested to solve three conditions above. The best confidence interval estimation and the forecasting accuracy are improved through adjusting of the left-right spreads of triangular fuzzy numbers. The yearly electricity load consumption of North-Taiwan from 1981 to 2000 are examined in evaluating the performance of three different left-right spreads of fuzzy random auto-regression models and some existing models, respectively. The result indicates that the smaller left-right spread of triangular fuzzy number provides the better forecast values if compared with based line models. Keywords: Fuzzy random variable, auto-regression model, left-right spread, triangular fuzzy number, forecasting error, electricity

An investigation of the suitability of Artificial Neural Networks for the prediction of core and local skin temperatures when trained with a large and gender-balanced database

Neural networks have been proven to successfully predict the results of complex non-linear problems in a variety of research fields, including medical research. Yet there is paucity of models utilising intelligent systems in the field of thermoregulation. They are under-utilized for predicting seemingly random physiological responses and in particular never used to predict local skin temperatures; or core temperature with a large dataset. In fact, most predictive models in this field (non-artificial intelligence based) focused on predicting body temperature and average skin temperature using relatively small gender-unbalanced databases or data from thermal dummies due to a lack of larger datasets. This paper aimed to address these limitations by applying Artificial Intelligence to create predictive models of core body temperature and local skin temperature (specifically at forehead, chest, upper arms, abdomen, knees and calves) while using a large and gender-balanced experimental database collected in office-type situations. A range of Neural Networks were developed for each local temperature, with topologies of 1–2 hidden layers and up to 20 neurons per layer, using Bayesian and the Levemberg-Marquardt back-propagation algorithms, and using various sets of input parameters (2520 NNs for each of the local skin temperatures and 1760 for the core temperature, i.e. a total of 19400 NNs). All topologies and configurations were assessed and the most suited recommended. The recommended Neural Networks trained well, with no sign of over-fitting, and with good performance when predicting unseen data. The recommended Neural Network for each case was compared with previously reported multi-linear models. Core temperature was avoided as a parameter for local skin temperatures as it is impractical for non-contact monitoring systems and does not significantly improve the precision despite it is the most stable parameter. The recommended NNs substantially improve the predictions in comparison to previous approaches. NN for core temperature has an R-value of 0.87 (81% increase), and a precision of ±0.46 °C for an 80% CI which is acceptable for non-clinical applications. NNs for local skin temperatures had R-values of 0.85-0.93 for forehead, chest, abdomen, calves, knees and hands, last two being the strongest (increase of 72% for abdomen, 63% for chest, and 32% for calves and forehead). The precision was best for forehead, chest and calves, with about ±1.2 °C, which is similar to the precision of existent average skin temperature models even though the average value is more stable

Machine Learning Algorithms for Very-Short Term Load Forecasting

Η ακριβής πρόβλεψη της ζήτησης ηλεκτρικής ενέργειας πολύ βραχυπρόθεσμα, είναι πολύ σημαντική για τη σταθερότητα και τον προγραμματισμό ανά ώρα με την ώρα ή την ημέρα, τη λειτουργία, τον προγραμματισμό του συστήματος ηλεκτρικής ενέργειας. Σε αυτή τη μελέτη, εστιάζουμε στην πρόβλεψη της ενεργού ισχύος για οικιακή κατανάλωση ηλεκτρικής ενέργειας, καθώς πιστεύουμε ότι, παρά τη συνολική πολυπλοκότητά τους, τα ακριβή μοντέλα υψηλής ευαισθησίας μπορούν να βοηθήσουν τις διαδικασίες ελέγχου στο περίπλοκο και σε μεγάλο βαθμό άγνωστο τοπίο των αγορών ηλεκτρικής ενέργειας, να οδηγήσουν σε ακριβείς αποτελέσματα. Κατά την ανάπτυξη αυτών των μοντέλων, χρησιμοποιούμε μοντέλα Machine Learning, Neural Networks και Time Series. Το σύνολο δεδομένων που χρησιμοποιούμε αποτελείται από δεδομένα κατανάλωσης ηλεκτρικής ενέργειας με ανάλυση λεπτού, κάποιων νοικοκυριών που ανακτήθηκαν από την πλατφόρμα UCI - The Center for Machine Learning and Intelligent Systems στο UC Irvine. Ο κύριος σκοπός αυτού του έργου είναι να συγκρίνει την απόδοση κάθε μοντέλου στο ίδιο σύνολο δεδομένων και να παρέχει χρήσιμες παρατηρήσεις σχετικά με τη διαδικασία εκπαίδευσης κάθε μοντέλου. Για αυτήν τη σύγκριση, θα προβλέψουμε την κατανάλωση ενέργειας μιας εβδομάδας, μίας ημέρας και μερικών ωρών και τέλος θα συγκρίνουμε τα μοντέλα. Υπάρχει πολύ λίγη βιβλιογραφία στον τομέα της πολύς βραχυπρόθεσμης πρόβλεψης και η μελέτη μας είναι μια από τις πρώτες συνοπτικές συγκρίσεις των τύπων νέων νευρωνικών δικτύων και ορισμένων μοντέλων Μηχανικής Μάθησης σε αυτόν τον ορίζοντα πρόβλεψης. Τα αποτελέσματά μας δείχνουν ότι τέτοια μοντέλα παλινδρόμησης και μοντέλα νευρωνικών δικτύων που χρησιμοποιήσαμε στο έργο μας για τα συγκεκριμένα δεδομένα λειτουργούν πολύ καλά. Η απόδοση των μοντέλων παλινδρόμησης εξαρτάται κυρίως από την ακρίβεια των χαρακτηριστικών και από την άλλη πλευρά, η απόδοση των νευρωνικών δικτύων εξαρτάται από την ομοιότητα δεδομένων μεταξύ της εκπαίδευσης και του συνόλου δοκιμών. Στο Κεφάλαιο 1, παρουσιάζουμε τα βασικά χαρακτηριστικά Machine Learning και τα μοντέλα Machine Learning που θα χρησιμοποιήσουμε στο έργο μας. Στο Κεφάλαιο 2, παρέχουμε μια επισκόπηση της ακαδημαϊκής έρευνας σχετικά με την πολύ βραχυπρόθεσμη πρόβλεψη φορτίου (VSTLF) με τη χρήση διαφορετικών μεθόδων Μηχανικής Μάθησης. Επιπλέον, σε αυτό το κεφάλαιο παρουσιάζουμε τις πλατφόρμες ανάπτυξης ανάλυσης δεδομένων και μηχανικής μάθησης και τις βασικές μετρήσεις αξιολόγησης μοντέλων. Στο Κεφάλαιο 3, εξηγούμε τα χαρακτηριστικά των δεδομένων, τη μεθοδολογία και τις έννοιες που χρησιμοποιήθηκαν για τη διεξαγωγή των προσομοιωμένων πειραμάτων. Επιπλέον, παρουσιάζουμε τα αποτελέσματα των πειραμάτων μας μέσω μετρήσεων αξιολόγησης που σχετίζονται με τη διαδικασία εκπαίδευσης και την ποιότητα πρόβλεψης κάθε μοντέλου. Επίσης, αναλύουμε και εντοπίζουμε την ποιότητα κάθε μεθόδου σε σύγκριση με τις άλλες και επιλέγουμε την καταλληλότερη για την περίπτωση χρήσης μας. Τέλος, στο Κεφάλαιο 4, συζητάμε τα αποτελέσματα που προέκυψαν και προτείνουμε κάποιες κατευθύνσεις για μελλοντική εργασία.Accurate electricity demand forecasting for a short horizon is very important for the stability and hour-to-hour or day-to-day scheduling, operation, planning of the power system. In this study, we focus on the minutely active power forecasting for residential electricity consumption, since we believe that, despite their overall complexity, accurate high granularity models can assist control procedures and, in the complex and largely unknown landscape of the electricity markets, lead to fine-grained price signal adjustments. In the development of those models, we use Machine Learning, Neural Networks and Time Series models. The dataset that we use consists of individual household’s electric power consumption minutely data retrieved from the UCI platform - The Center for Machine Learning and Intelligent Systems at UC Irvine. The main purpose of this project is to compare the baseline performance of each model on the same dataset and provide useful remarks on the training process of each model. For this comparison, we will forecast one week’s power consumption with minutely resolution and finally compare the used models. There is little work in the area of minute power forecasting and our study is one of the first concise comparisons of the core neural network types and of some Machine Learning models on this prediction horizon with experiments conducted on residential active power data. Our results show that such the regression models and the neural network models that we used in our project for the specific data operates very well. The regression models performance mainly depends on the features accuracy and one the other side, neural networks performance depends on the pattern similarity between the training and the testing set. In Chapter 1, we present the basic Machine Learning characteristics and the Machine Learning models that we will use in our project. In Chapter 2, we provide an overview of the academic research on Very Short-Term Load Forecasting (VSTLF) with the use of different of Machine Learning methods. The different case studies used in those studies do not allow a comparison of their results. Furthermore, in this chapter we present the data analysis and machine learning development platforms and the basic model evaluation metrics. In Chapter 3, we explain the datasets characteristics, the methodology and the concepts that were used to conduct the simulated experiments. Moreover, we present the results of our experiments through evaluation metrics relevant to the training process and the prediction quality of each model. Also, we analyze and identify the quality of each method compared to the others and select the most suitable for our use case. Finally, in Chapter 4, we discuss the results obtained and suggest some directions for future work