Short-term electricity load demand forecast is a vital requirements for power systems. This research considers the combination of exponential smoothing for double seasonal patterns and neural network model. The linear version of Holt-Winter method is extended to accommodate a second seasonal component. In this work, the Fourier with time varying coefficient is presented as a means of seasonal extraction. The methodological contribution of this paper is to demonstrate how these methods can be adapted to model the time series data with multiple seasonal pattern, correlated non stationary error and nonlinearity components together. The proposed hybrid model is started by implementing exponential smoothing state space model to obtain the level, trend, seasonal and irregular components and then use them as inputs of neural network. Forecasts of future values are then can be obtained by using the hybrid model. The forecast performance was characterized by root mean square error and mean absolute percentage error. The proposed hybrid model is applied to two real load series that are energy consumption in Bawen substation and in Java-Bali area. Comparing with other existing models, results show that the proposed hybrid model generate the most accurate forecast

This study aims to predict the population dynamics of Brown Planthopper (BPH) in highly endemic areas of Central Java province, Indonesia. The research was conducted by modifying the method proposed by Legendre and Fortin (1989), through three stages. Those were predicting BPH attacks using Exponential Smoothing Holt Winter, analyzing spatial structure using I, C and Z test on Local Statistic, and making the connectivity inter the periodic predictions of planting season. The results showed that, the studied areas will experience the hotspots phenomenon based on the analysis by the method of Moran's I, Geary's C and Getis Ord Statistic. The analysis of Local Moran's and Getis Ord showed that, four counties namely Boyolali, Klaten, Karanganyar and Sragen experienced a local migration current from region to region around them, whereas other counties are independent. The migration current was influenced by topography, biotic interactions, and anthropogenic factor. Viewed from the spatial scalability in the studied areas, there are four categories of BPH population distribution; point, site, local, and landscape. BPH local migration interregion happened in the County of Klaten, Boyolali, Karanganyar and Sragen. It was caused by some factors: (1) the local climate, (2) the repetition of the use of rice plant variety in a long time, (3) the use of insecticide intensively (3-4 times in one planting period/season), and (4) the irrigation, allowing the spread of BPH larvae and eggs into its surroundings.

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This study attempted to combine SSA (Singular Spectrum Analysis) with other methods to improve the performance of forecasting model for time series with a complex pattern. This work discussed two modifications of TLSAR (Two-Level Seasonal Autoregressive) modeling by considering the SSA decomposition results, namely TLSNN (Two-Level Seasonal Neural Network) and TLCSNN (Two-Level Complex Seasonal Neural Network). TLSAR consisted of a linear trend, harmonic, and autoregressive component. In contrast, the two proposed hybrid approaches consisted of flexible trend function, harmonic, and neural networks. Trend and harmonic function were considered as the deterministic part identified based on SSA decomposition. Meanwhile, NN was intended to handle the nonlinearity relationship in the stochastic part. These two SSA-based hybrid models were contemplated to be more flexible than TLSAR and more applicable to the series with an intricate pattern. The experimental studies to the monthly accidental deaths in USA and daily electricity load Jawa-Bali showed that the proposed SSA-based hybrid model reduced RMSE for the testing data from that obtained by TLSAR model up to 95%.

Abstract