Analyzing Seasonality in Hydropower Plants Energy Production and External Variables

Gjika, Eralda; Basha, Lule; Ferrja, Aurora; Kamberi, Arbesa

doi:10.3390/engproc2021005015

Cited by 3 publications

(4 citation statements)

References 26 publications

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“…What is noticed in recent years in the Mediterranean region and in Albania is also the fact that based on the above factors the utilization rate of hydropower plants has decreased. This decline has been followed by an increase in interest in solar energy which is mainly influenced by surface solar radiation whose variations depend mainly on the atmospheric composition (aerosols, water vapor) and clouds [2], [3]. An increase in solar radiation has been observed in Europe [4] and especially for the Mediterranean basin these solar sources are seen with special interest as one of the areas with medium to high solar radiation on the continent [5].…”

Section: Introductionmentioning

confidence: 99%

“…There is also a lot of work done especially in machine learning (ML) techniques which A comparative study of statistical and deep learning models for energy load prediction rely on historic data and are extensively used to short-term forecasting [9]. Classical statistical techniques on energy prediction are often used as a benchmark for many techniques but in many cases depending on the nature of the data used and exogenous variables these techniques perform comparable with engineering-based models or ML models [2], [3].…”

Section: Introductionmentioning

confidence: 99%

“…They point out that the accuracy of the ANNs' prediction depends on the quality and availability of the training data. An analysis of the accuracy of ML and statistical techniques for Albanian energy sector is done by [2]. In their work they consider the energy production by hydropower's which is the main source of production in the country.…”

Section: Introductionmentioning

confidence: 99%

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A Comparative Study of Statistical and Deep Learning Models for Energy Load Prediction

Gjika

Basha

2022

International Journal of Electrical Engineering and Computer Science

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The objective of this study is to analyze and compare classical time series and deep learning models for energy load prediction. Energy predictions are important for management and sustainable systems. After analyzing the climacteric factors impact on energy load (a case study in Albania) we considered classical and deep learning models to perform forecasts. We have used hourly and daily time series for a period of three years. In total respectively 26,280 hours and 1095 days. Average temperature is considered as external variable in both statistical and deep learning models. The dynamic evolution of hourly (daily) load is correlated with hourly (daily) average temperature. The performance of the proposed models is analyzed and evaluated based on accuracy measurements (MSE, RMSE, MAPE, AIC, BIC etc.) and graphics results of statistical tests. In-sample and out-of-sample accuracy is evaluated. The models show competitive performance to some recent works in the field of short-and medium-term energy load forecasts. This work may be used by stakeholders to optimize their activities and obtain accurate forecasts of energy system behavior.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Comparative Study of Statistical and Deep Learning Models for Energy Load Prediction

Gjika

Basha

2022

International Journal of Electrical Engineering and Computer Science

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“…They constructed different statistical models and machine learning with the data which included: weather, population, location, and time in its models and forecasts, all of which are autoregressive components of historical load data. Gjika et al (2021) analyzed the seasonal pattern of climacteric factors: precipitation, average temperature, and water inflow affecting energy production. They considered different statistical learning methods for energy prediction: ARIMA, ETS, NN, TBATS, STLM, etc.…”

Section: Introductionmentioning

confidence: 99%

Climate Change and its Effect on the Energy Production from Renewable Sources – A Case Study in Mediterranean Region

Gjika¹,

Basha²,

Sokoli³

2022

J. Ecol. Eng.

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In terms of climate forecasting, the Mediterranean region is among the most difficult. It is correlated with the five significant subtropical high pressure belts of the oceans and is symbolized by dry and hot summer and cold and rainy winter. Due to its location in the area, Albania is particularly susceptible to climatic changes. It has been noted that summertime sees the greatest temperature increases. More intense heat waves that stay longer and occur more frequently are anticipated in the eastern Mediterranean. The seasonal patterns of precipitation have not changed, but the amount of rain has become more intense. The effects of climate change have drawn attention to various renewable energy sources, including solar and wind power. In this study, the changes and prospective in average temperature, rainfall, humidity, CO 2 emission and their impact in energy production were investigated. Several different models such as Auto Regressive Integrated Moving Average method; Prophet algorithm; Elastic-Net Regularized Generalized Linear Model; Random Forest Regression models; Prophet Boost algorithm; have been built for the study and prediction of each variable. The appropriate models are used to determine the anticipated values of the indicators for a period of four years. The prediction shows an increase in CO 2 emission which leads to a decrease in energy production by hydropower. These findings suggest the use of other renewable sources for energy production in the country and the Mediterranean region.

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Artificial Intelligence RES Forecasting Based on Weather Data Time

Tanev

Stanev

2021

2021 13th Electrical Engineering Faculty Conference (BulEF)

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Analyzing Seasonality in Hydropower Plants Energy Production and External Variables

Cited by 3 publications

References 26 publications

A Comparative Study of Statistical and Deep Learning Models for Energy Load Prediction

A Comparative Study of Statistical and Deep Learning Models for Energy Load Prediction

Climate Change and its Effect on the Energy Production from Renewable Sources – A Case Study in Mediterranean Region

Artificial Intelligence RES Forecasting Based on Weather Data Time

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