Climatic changes have a significant impact on many real life processes. Climacteric position of Albania makes precipitations and water inflows in HPP the main variables influencing the amount of electric energy produced in the country. Taking into account their volatility it has considerably increased the need of using hybrid models to improve the quality of predictions. After a detailed analysis of the time series components, we develop a group of hybrid models and propose modifications to increase the accuracy in prediction. Among the contributions of this work is the challenge to choose between hybrid models presented earlier in literature and the modified version according to the nature of data. The final decision on the most accurate model is made based on many goodness of fit test. This study suggest that an accurate selection of the forecasting techniques increases significantly the quality of forecast on precipitations and water inflow data.
This study is focused on energy production in Albania which involves different types of infrastructure at the various points of the energy production and distribution chain, as well as monitoring and early warning systems. At a time of rapid climate change, estimating the appropriate dimensions and design of such infrastructure and systems becomes crucial. The main objective is to analyze the seasonality pattern and main external climacteric factors, such as precipitation, average temperature, and water inflow. This work deals with the seasonality patterns of climacteric factors affecting energy production and considers different statistical learning methods for prediction.
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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