A comparative study of artificial neural network and ANFIS for short term load forecasting

Çevik, Hasan Hüseyin; Çunkaş, Mehmet

doi:10.1109/ecai.2014.7090206

Cited by 10 publications

(2 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The used methods can be divided into three as statistical methods, artificial intelligence methods and hybrid methods. The most commonly used techniques are based on Regression models [4], Times series models [5], [6], ARIMA models [7], Artificial Neural Network models [8], [9], Fuzzy models [10], [11], Support vector machine models [12], Particle swarm optimization models [13], Genetic algorithm models [14], [15], wavelet transform [16], ANFIS [11]. This paper focuses on a hybrid method based on the combination of artificial bee colony (ABC) and artificial neural network (ANN) for short term load forecasting.…”

Section: Introductionmentioning

confidence: 99%

Short-term Load Forecasting based on ABC and ANN for Smart Grids

Çevik

Harmanci²

2016

ijisae

Self Cite

View full text Add to dashboard Cite

Short term load forecasting is a subject about estimating future electricity consumption for a time interval from one hour to one week and it has a vital importance for the operation of a power system and smart grids. This process is mandatory for distribution companies and big electricity consumers, especially in liberalized energy markets. Electricity generation plans are made according to the amount of electricity consumption forecasts. If the forecast is overestimated, it leads to the start-up of too many units supplying an unnecessary level of reserve, therefore the production cost is increased. On the contrary if the forecast is underestimated, it may result in a risky operation and consequently power outages can occur at the power system. In this study, a hybrid method based on the combination of Artificial Bee Colony (ABC) and Artificial Neural Network (ANN) is developed for short term load forecasting. ABC algorithm is used in ANN learning process and it optimizes the neuron connections weights of ANN. Historical load, temperature difference and season are selected as model inputs. While three years hourly data is selected as training data, one year hourly data is selected as testing data. The results show that the application of this hybrid system produce forecast values close to the actual values.

show abstract

Section: Introductionmentioning

confidence: 99%

Short-term Load Forecasting based on ABC and ANN for Smart Grids

Çevik

Harmanci²

2016

ijisae

Self Cite

View full text Add to dashboard Cite

show abstract

“…ANFIS also leads comparative analysis with other algorithms for classification [53] [54]. Additionally, ANFIS is a reliable system with relatively high degree of accuracy [55], has very low mean absolute percentage error [56], has the ability to train quickly with just a few epochs [57] and efficient with an extreme standard of accuracy and using minimum need of data set samples. ANFIS is very useful when solving complex problems especially for technical diagnostics and measurement assignments [58].…”

mentioning

confidence: 99%

Adaptive Neuro-Fuzzy Inference System Approach on Predicting Hard Disk Failures towards Reliable Data Center

Olalia¹

2018

IJRASET

View full text Add to dashboard Cite

Despite the high accuracy showcased by some studies in predicting hard disk failure using decision tree in the classification process, the accuracy of the decision tree is in question due to its vast difference among other algorithms ranging from 17.7% to 60.92%. This paper confirms the claim of some studies about the overfitting of decision tree when used with a large amount of data, real-valued and numeric attributes like SMART at-tributes. Utilizing ANFIS algorithm in predicting imminent hard disk failure surpasses other algorithms (CHAID, C&R Tree, Neural Network, MLR, and SVM) by 4.2% while keeping its distance from the very high percentage (99.58) over-fitted decision tree at 86.08%. The ANFIS also predicted the failure 5 days before it actually happens. Keywords: ANFIS, Predicting Hard Disk Failure, SMART Attributes, Data Center, Imminent Disk Failure I. INTRODUCTIONHaving files rested and backed up on a data center or a cloud storage platform ensures continuous operation, data protection and recovery [1]. It is the responsibility of the data center to do all their necessary plans and procedures to be so. Critical factors such as experience, financial stability, security, support and physical infrastructure need to be maintained to have a reliable data center [2]. In the Philippines, 80% of business enterprises experienced data loss costing them around $8 billion worth of data loss [3]. On a larger scale, companies around the world experienced an average data loss of 400% in just two years accumulating a total amount loss of $1.7 trillion, 30% of which came from cloud storage. Admittedly, 51% of surveyed companies have no disaster recovery plan [4]. Study shows that the leading cause of data loss is the hardware failure, human error, software corruption, computer viruses and natural disaster among which hardware failure rank first at 57% [5]. Recent studies conducted by Data Barracks shows that hardware failure is still one of the topmost cause of data loss at 25%, being human error as the leader at 29% [6]. With the help of machine learning, failure can be preempted thereby avoiding data loss before it happens. There are some researches delving into this problem [7][8] [9][10][11] [12]. Each research presented varying results. Among these different researches and algorithms used, decision tree came out as the most accurate. Similarly, Suchatpong and Bhumkittipich's study com-pared decision tree with the neural network, SVM, CHAID and C&R Tree. Decision tree ranks first at 99.58% while neural network is at 56.09% accurate, SVM is at 38.66%, CHAID at 50.42% and C&R tree at 56.93%, which are way lower that the decision tree. While the result of the decision tree is promising, there is an observable irregularity in the result. The decision tree is too accurate compare to other algorithm in predicting hard drive failure. IBM states that if an algorithm has 98% accuracy while other techniques tried has 60% accuracy, it is most probably overfitting, which is exactly the case [13]. Initial investigatio...

show abstract