A Comparative Analysis of Machine Learning Approaches for Short-/Long-Term Electricity Load Forecasting in Cyprus

Abstract

Estimating the electricity load is a crucial task in the planning of power generation systems and the efficient operation and sustainable growth of modern electricity supply networks. Especially with the advent of smart grids, the need for fairly precise and highly reliable estimation of electricity load is greater than ever. It is a challenging task to estimate the electricity load with high precision. Many energy demand management methods are used to estimate future energy demands correctly. Machine learning methods are well adapted to the nature of the electrical load, as they can model complicated nonlinear connections through a learning process containing historical data patterns. Many scientists have used machine learning (ML) to anticipate failure before it occurs as well as predict the outcome. ML is an artificial intelligence (AI) subdomain that involves studying and developing mathematical algorithms to understand data or obtain data directly without relying on a prearranged model algorithm. ML is applied in all industries. In this paper, machine learning strategies including artificial neural network (ANN), multiple linear regression (MLR), adaptive neuro-fuzzy inference system (ANFIS), and support vector machine (SVM) were used to estimate electricity demand and propose criteria for power generation in Cyprus. The simulations were adapted to real historical data explaining the electricity usage in 2016 and 2107 with long-term and short-term analysis. It was observed that electricity load is a result of temperature, humidity, solar irradiation, population, gross national income (GNI) per capita, and the electricity price per kilowatt-hour, which provide input parameters for the ML algorithms. Using electricity load data from Cyprus, the performance of the ML algorithms was thoroughly evaluated. The results of long-term and short-term studies show that SVM and ANN are comparatively superior to other ML methods, providing more reliable and precise outcomes in terms of fewer estimation errors for Cyprus’s time series forecasting criteria for power generation.

Electricity Consumption Forecasting Based on a Bidirectional Long-Short-Term Memory Artificial Neural Network

“…In order to attain these goals, scientists have used different methods and carried out numerous performance improvement techniques, such as: SVR [3,11,28], LSTM ANNs [3,4,10,11,26,28], RNN [5,28], BiLSTM ANN [5], Copula-DBN [7], DRNN-LSTM [8], CNN-RNN [9], the Prophet and Holt-Winters long-term forecasting models [13], CNNs [4,6,14,25], MLR [24,25], SVM [24], and ANFIS [24]. In contrast with these, within our article, we devised and developed a forecasting method for the hourly month-ahead electricity consumption based on a BiLSTM ANN enhanced with a multiple simultaneously decreasing delays approach coupled with FITNETs.…”

“…Analyzing the scientific literature, one can remark that when forecasting the electricity consumption, different forecasting time horizons are of interest, encompassing short- [3,14,19,22,24,26,28], medium- [19,21,26], and long-term timeframes [10,13,14,20,[23][24][25]64], each of them bringing their own particular advantages in line with the actual requirements and business needs of the contractors. We targeted the hourly month-ahead electricity prediction, considering the numerous benefits that such a forecast brings to the largeelectricity commercial center-type consumer, ranging from the negotiation and choosing of the most appropriate hourly billing tariffs and correct estimations for the month-ahead electricity consumption submitted to the dispatcher to proper decisional support in what concerns the return on investment in more energy efficient equipment and assessing expanding options.…”

“…Being aware of the importance of electricity load forecasting in view of planning efficient operation of supply networks, within the scientific article [24], Sloyali implemented a series of machine learning methods, namely ANN, MLR, adaptive neuro-fuzzy inference system (ANFIS), and support vector machine (SVM) with the purpose of estimating energy demand, and discussed criteria related to its generation in Cyprus. The author remarked that the electricity load depends on external parameters such as weather, humidity, and solar irradiation, but also on national factors, obtaining therefore the parameters used as inputs for the machine learning algorithms.…”

A Comparison Framework of Machine Learning Algorithms for Mixed-Type Variables Datasets: A Case Study on Tire-Performances Prediction

“…Their method is compared against five popular regression algorithms and, similar to us, they perform a statistical analysis of the performances of such methods over multiple datasets, but they used a single dummy variable encoding. More recent deep learning approaches for regression problems have proven to be very efficient in diverse application such as industrial surface defect detection [22], sustainable smart manufacturing in industry 4.0 [23] and short-long term load electricity forcasting [24]. Unlike the mentioned approaches, we present a rigorous statistical-based framework to compare and recommend regression algorithms considering many strategies to handle appropriately mixed variables over multiple datasets.…”

Modelling Using Machine Learning Models and Prediction of Monthly Global Irradiation

“…Possibly, the three principal groups of models used to forecast solar radiation are machine learning, physical (or numerical weather prediction) and sky imaging [12]. Machine learning is an artificial intelligence subfield that studies and develop mathematical algorithms intended to comprehend data and obtain data without a prearranged model algorithm [13]. The machine learning models can find the relationship between inputs and outputs variables, which allow that these models can be used, sometimes, in classification problems, forecasting problems, among others [14].…”