In this work, we try to solve the problem of day-ahead prediction of electricity demand using an ensemble forecasting model. Based on the Pattern Sequence Similarity (PSF) algorithm, we implemented five forecasting models using different clustering techniques: K-means model (as in original PSF), Self-Organizing Map model, Hierarchical Clustering model, K-medoids model, and Fuzzy C-means model. By incorporating these five models, we then proposed an ensemble model, named Pattern Forecasting Ensemble Model (PFEM), with iterative prediction procedure. We evaluated its performance on three real-world electricity demand datasets and compared it with those of the five forecasting models individually. Experimental results show that PFEM outperforms all those five individual models in terms of Mean Error Relative and Mean Absolute Error.
Working memory function is severely limited. One key limitation that constrains the ability to maintain multiple items in working memory simultaneously is so-called swap errors. These errors occur when an inaccurate response is in fact accurate relative to a non-target stimulus, reflecting the failure to maintain the appropriate association or “binding” between the features that define one object (e.g., color and location). The mechanisms underlying feature binding in working memory remain unknown. Here, we tested the hypothesis that features are bound in memory through synchrony across feature-specific neural assemblies. We built a biophysical neural network model composed of two one-dimensional attractor networks – one for color and one for location – simulating feature storage in different cortical areas. Within each area, gamma oscillations were induced during bump attractor activity through the interplay of fast recurrent excitation and slower feedback inhibition. As a result, different memorized items were held at different phases of the network’s oscillation. These two areas were then reciprocally connected via weak cortico-cortical excitation, accomplishing binding between color and location through the synchronization of pairs of bumps across the two areas. Encoding and decoding of color-location associations was accomplished through rate coding, overcoming a long-standing limitation of binding through synchrony. In some simulations, swap errors arose: “color bumps” abruptly changed their phase relationship with “location bumps.” This model, which leverages the explanatory power of similar attractor models, specifies a plausible mechanism for feature binding and makes specific predictions about swap errors that are testable at behavioral and neurophysiological levels.
Abstract-In this paper, two models were proposed for week-ahead forecasting of temperature driven electricity load, which are a time series model and an Artificial Neural Network (ANN) model. Over the week-long ("future") forecasting horizon, predicted temperature from ANN was used as it is shown that ANN produced more accurate temperature prediction. For the time series model, Seasonal Autoregressive Integrated Moving Average with eXogenous variables (SARIMAX) scheme was proposed. A method called "pre-whitening" was used to determine the lagged effect of temperature on electricity load. Comparison between ANN model and SARIMAX model was conducted to see which one gave a better forecasting performance. The forecast performance was characterized by two statistical estimates, the Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE). The results showed that while the ANN model behaved better in the estimation stage, its performance got worse than SARIMAX model in the forecasting stage.Index Terms-Artificial neural networks (ANN), load forecasting, SARIMAX, short-term, temperature forecasting, time series. I. INTRODUCTIONAfter the deregulation of electricity markets, electricity was commoditized. As a result, the generation of electricity more flexible and demand oriented. However, there are also risks associated the deregulation of electricity markets such as electricity oversupply and shortage due to inaccurate forecasting, which could result in significant financial loss. That is why accurate electricity forecasting plays a very important role and could also improve power generation planning. In this study two kinds of models, SARIMAX and ANN, were proposed for short-term forecasting of temperature driven electricity load forecasting.Different approaches have been proposed for the short-term forecasting of electricity load. Generally speaking, these approaches can be grouped into three categories: regression-based, time series, artificial intelligence and computational intelligence. The latter can divided into several sub-groups, such as neural networks, support vector machines, hybrid and other approaches. In the following section, mainly neural networks and time series approaches will be studied from the literature.Ghanbari et al. et al. [9] and Martí nez-Álvarez [10] all proposed an approach based on selection of similar days according to which the load curves are forecasted by using the information of the days being similar to that of the forecast day.Choi et al. [11] and Kutluk et al. [12] both proposed the classic SARIMA method for load forecasting while James Taylor extended double seasonal ARMA model which includes intraday and intraweek seasonal cycles to include intrayear seasonal cycle, which is also apparent if one disposes of a multi-year training dataset. Weather features were also used to construct a classic ARMA/SARIMA model, which can be found in Jennifer et al. 's work. [5] G. Peter [13] proposed a hybrid methodology that combines both ARIMA and ANN models to take advantage ...
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The high population density in cities confers many advantages, including improved social interaction and information exchange. However, it is often argued that urban living comes at the expense of reducing happiness. The goal of this research is to shed light on the relationship between urban communication and urban happiness. We analyze geo-located social media posts (tweets) within a major urban center (Milan) to produce a detailed spatial map of urban sentiments. We combine this data with high-resolution mobile communication intensity data among different urban areas. Our results reveal that happy (respectively unhappy) areas preferentially communicate with other areas of their type. This observation constitutes evidence of homophilous communities at the scale of an entire city (Milan), and has implications on interventions that aim to improve urban well-being.
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