This study investigates the hourly prices of the electricity day-ahead market nonstationary characteristics and long-range correlation. Using the detrended fluctuation analysis (DFA) approach, we show evidence of long memory for the Bulgarian day-ahead market between January 20th, 2016 and December 31, 2019. Furthermore, the results from the DFA methodology shows that behavior of the hourly electricity spot prices returns were long term positively correlated. DFA methods can be used as powerful tools for analyzing very volatile series like electricity prices considering the fact that in recent years prices become more volatile due to increased integration of renewables that are intermittent and far more volatile than other commodities normally considered with extreme volatility. Day ahead electricity prices are crucially important for forecasting, derivatives pricing and risk management and therefore in this paper we give a brief introduction on DFA method.

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The availability of accurate day-ahead electricity price forecasts is very important for electricity market participants and it is an essential challenge to accurately forecast the electricity price. Therefore, this study proposes an efficient method suitable for electricity price forecasting (EPF) and processing time-series data from the Bulgarian day-ahead market based on a long-short term memory (LSTM) recurrent neural network model. The LSTM model is used to forecast the day-ahead electricity price for the Bulgarian day-ahead market. As inputs to the model are used historical hourly prices for the period between 20.01.2016 and 05.03.2022. The output is the electricity price forecasts for hours and days ahead. The future values of prices are forecasted recursively. LSTM can model temporal dependencies in larger Time Series set horizons without forgetting the short-term patterns. LSTM networks are composed of units that are called LSTM memory cells and these cells contain some gates that process the inputs. Since electricity price is affected by various seasonal effects, the model is trained for several years. The effectiveness of the proposed method is verified using real market data.

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Electricity price forecasting becomes a significant challenge on a day-to-day basis and price variations are even more volatile on an hourly basis. Therefore, this paper is used several approaches to analyze the Bulgarian hourly electricity price dynamics in the day-ahead market. Proper analysis crucially depends on the choice of an adequate model. Reviewed are the factors which may influence the electricity spot prices and characteristics of the time series of prices. Methods include and variety of modeling approaches that are applied and evaluated for forecasting electricity prices such as time-series models and regression models. The forecasting technique is to model day-ahead spot prices using well known ARIMA/SARIMA model including stationarity checks, seasonal decompose, differencing, autoregressive modeling, and autocorrelation to analyze and forecast time series hourly data. For each approach, model estimates and forecasts are developed using hourly price data, reshaped, and aggregated data on a daily and monthly basis for the Bulgarian day-ahead market.