We present our Zero Energy Network'' (ZEN) protocol stack for energy harvesting wireless sensor networks applications. The novelty in our work is @math fold: (1) Energy harvesting aware fully featured MAC layer. Carrier sensing, Backoff algorithms, ARQ, RTS CTS mechanisms, Adaptive Duty Cycling are either auto configurable or available as tunable parameters to match the available energy (b) Energy harvesting aware Routing Protocol. The multi-hop network establishes routes to the base station using a modified version of AODVjr routing protocol assisted by energy predictions. (c) Application of a time series called Holt-Winters'' for predicting the incoming energy. (d) A distributed smart application running over the ZEN stack which utilizes a multi parameter optimized perturbation technique to optimally use the available energy. The application is capable of programming the ZEN stack in an energy efficient manner. The energy harvested distributed smart application runs on a realistic solar energy trace with a three year seasonality database. We implement a smart application, capable of modifying itself to suit its own as well as the network's energy level. Our analytical results show a close match with the measurements conducted over EHWSN testbed.
Accurate prediction of the energy is an important factor for energy neutral operations. Time series based energy predictions are commonly used for predicting solar energy. Exponential Weighted Moving Average (EWMA) is commonly applied for future energy predictions @cite_8 , @cite_18 and assigns weights to data in the time series, assigning lower weights to older data and giving importance to more recently acquired data. Holt-Winters (HW) time series is widely used in stock markets trend analysis, production planning, healthcare decisions on staffing & purchasing, demand forecasting @cite_12 and for predicting share prices where forecast value takes trend and seasonal variation into account @cite_4 , @cite_2 . In @cite_26 a three state markov chain model is used to predict the solar energy source and their transition probabilities are restricted for day time only. Energy samples from a simulation model are also generated from the model.
@cite_18 @cite_26 @cite_4 @cite_8 @cite_2 @cite_12
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Abstract This paper proposes a stochastic and dynamic mixed-integer linear program (SD-MILP) for optimal coordinated bidding of a risk-averse profit-maximizing hydropower producer. The day-ahead, intra-day, and real-time markets are considered. To model and predict day-ahead, intra-day, and real-time prices, the Holt–Winter (HW) and the Generalized Autoregressive Conditional Heteroscedastic (GARCH) predictors are combined using a proposed Markov switch. The discrete behavior of intra-day and real-time prices is modeled as different Markov states. The proposed Markov-based HW–GARCH model with a standard scenario generation-reduction technique is used to capture the uncertainty in day-ahead, intra-day, and real-time prices. The time-dependent conditional value at risk (T-CVaR) is proposed to model the risk of trading in different considered markets. The convex combination of the expected profit and T-CVaR is used as the objective of SD-MILP. The Markov-based HW–GARCH is modeled in Matlab and the SD-MILP is coded in GAMS. The Markov-based HW–GARCH predictor and the SD-MILP are used to develop the bidding curve of a three-reservoir hydropower producer using the electricity prices from the Nordic power market. To further examine the developed models a seven-reservoir hydropower producer is also studied. For these two cases, the coordinated bidding curves are derived and discussed. Recently, Wireless Sensor Networks (WSNs) begin to use the solar energy. But the energy from the environment is usually unstable, An efficient solar prediction algorithm should be studied. In this paper, a novel solar energy prediction algorithm, namely Weather-Conditioned Selective Moving Average (WCSMA), is proposed by using the trend similarity of energy harvesting and the classification of sunny and cloudy days. The simulation results show that the relative mean error of WCSMA algorithm only around 10 , and it is much lower than the Exponential Weighted Moving Average (EWMA) algorithm which is widely used now. This paper considers univariate online electricity demand forecasting for lead times from a half-hour-ahead to a day-ahead. A time series of demand recorded at half-hourly intervals contains more than one seasonal pattern. A within-day seasonal cycle is apparent from the similarity of the demand profile from one day to the next, and a within-week seasonal cycle is evident when one compares the demand on the corresponding day of adjacent weeks. There is strong appeal in using a forecasting method that is able to capture both seasonalities. The multiplicative seasonal ARIMA model has been adapted for this purpose. In this paper, we adapt the Holt-Winters exponential smoothing formulation so that it can accommodate two seasonalities. We correct for residual autocorrelation using a simple autoregressive model. The forecasts produced by the new double seasonal Holt-Winters method outperform those from traditional Holt-Winters and from a well-specified multiplicative double seasonal ARIMA model. Realistic time-resolved data on occupant behaviour, presence and energy use are important inputs to various types of simulations, including performance of small-scale energy systems and buildings' indoor climate, use of lighting and energy demand. This paper presents a modelling framework for stochastic generation of high-resolution series of such data. The model generates both synthetic activity sequences of individual household members, including occupancy states, and domestic electricity demand based on these patterns. The activity-generating model, based on non-homogeneous Markov chains that are tuned to an extensive empirical time-use data set, creates a realistic spread of activities over time, down to a 1-min resolution. A detailed validation against measurements shows that modelled power demand data for individual households as well as aggregate demand for an arbitrary number of households are highly realistic in terms of end-use composition, annual and diurnal variations, diversity between households, short time-scale fluctuations and load coincidence. An important aim with the model development has been to maintain a sound balance between complexity and output quality. Although the model yields a high-quality output, the proposed model structure is uncomplicated in comparison to other available domestic load models. The increasing popularity of micro-scale power-scavenging techniques for wireless sensor networks (WSNs) is paving the way to energy-autonomous sensing systems. To sustain perpetual operations, however, environmentally powered devices must adapt their workload to the stochastic nature of ambient sources. Energy prediction models, which estimate the future expected energy intake, are effective tools to support the development of proactive power management strategies. In this paper, we present profile energy prediction model (Pro-Energy), an energy prediction model for multi-source energy-harvesting WSNs that leverages past energy observations to forecast future energy availability. We then propose Pro-Energy with variable-length timeslots (Pro-Energy-VLT), an extension of Pro-Energy that combines our energy predictor with timeslots of variable lengths to adapt to the dynamics of the power source. To assess the performance of our proposed solutions, we use real-life solar and wind traces, as well as publicly available traces of solar irradiance and wind speed. A comparative performance evaluation shows that Pro-Energy significantly outperforms the state-of-the-art energy predictors, by improving the prediction accuracy of up to 67 . Moreover, by adapting the granularity of the prediction timeslots to the dynamics of the energy source, Pro-Energy-VLT further improves the prediction accuracy, while reducing the memory footprint and the energy overhead of energy forecasting. This paper presents a new method based on the cost-benefit analysis for optimal sizing of an energy storage system in a microgrid (MG). The unit commitment problem with spinning reserve for MG is considered in this method. Time series and feed-forward neural network techniques are used for forecasting the wind speed and solar radiations respectively and the forecasting errors are also considered in this paper. Two mathematical models have been built for both the islanded and grid-connected modes of MGs. The main problem is formulated as a mixed linear integer problem (MLIP), which is solved in AMPL (A Modeling Language for Mathematical Programming). The effectiveness of the approach is validated by case studies where the optimal system energy storage ratings for the islanded and grid-connected MGs are determined. Quantitative results show that the optimal size of BESS exists and differs for both the grid-connected and islanded MGs in this paper. In this paper, the modeling of building end-use energy profile is comprehensively investigated. Top-down and Bottom-up approaches are discussed with a focus on the latter for better integration with occupant information. Compared to the Time-Of-Use (TOU) data used in previous Bottom-up models, this work utilizes high frequency sampled appliance power consumption data from wireless sensor network, and hence builds an appliance-data-driven probability based end-use energy profile model. ON OFF probabilities of appliances are used in this model, to build a non-homogeneous Markov Chain, compared to the duration statistics based model that is widely used in other works. The simulation results show the capability of the model to capture the diversity and variability of different categories of end-use appliance energy profile, which can further help on the design of a modern robust building power system. Solar panels are frequently used in wireless sensor nodes because they can theoretically provide quite a bit of harvested energy. However, they are not a reliable, consistent source of energy because of the Sun's cycles and the everchanging weather conditions. Thus, in this paper we present a fast, efficient and reliable solar prediction algorithm, namely, Weather-Conditioned Moving Average (WCMA) that is capable of exploiting the solar energy more efficiently than state-of-the-art energy prediction algorithms (e.g. Exponential Weighted Moving Average EWMA). In particular, WCMA is able to effectively take into account both the current and past-days weather conditions, obtaining a relative mean error of only 10 . When coupled with energy management algorithm, it can achieve gains of more than 90 in energy utilization with respect to EWMA under the real working conditions of the Shimmer node, an active sensing platform for structural health monitoring. An accurate forecast of the exploitable energy from Renewable Energy Sources, provided 24 h in advance, is becoming more and more important in the context of the smart grids, both for their stability issues and the reliability of the bidding markets. This work presents a comparison of the PV output power day-ahead forecasts performed by deterministic and stochastic models aiming to find out the best performance conditions. In particular, we have compared the results of two deterministic models, based on three and five parameters electric equivalent circuit, and a hybrid method based on artificial neural network. The forecasts are evaluated against real data measured for one year in an existing PV plant located at SolarTechlab in Milan, Italy. In general, there is no significant difference between the two deterministic models, being the three-parameter approach slightly more accurate (NMAE three-parameter 8.5 vs. NMAE five-parameter 9.0 ). The artificial neural network, combined with clear sky solar radiation, generally achieves the best forecasting results (NMAE 5.6 ) and only few days of training are necessary to provide accurate forecasts. Abstract In this paper, a model for the synthetic generation of 1-min global solar radiation data starting from the daily clearness index is presented. The model is constructed by treating the process generating a normalized form of the 1-min clearness index sequence as a Markov process. Two sets of three-year global solar radiation data taken at 1-min intervals from two locations in Japan are used to construct the Markov transition matrices. Because different days have different statistical characteristics due to different weather conditions, the days in the data set are first clustered into groups based on the daily clearness index values. Transition matrices are then formed for each group and consequently used to synthetically generate 1-min global solar radiation data. Second-order Markov models are selected based on the partial autocorrelation functions of the measured data. The statistical characteristics of the measured and synthetic data sets are found to be in close agreement thus confirming the validity of the model. A suitable combination of linear and nonlinear models provides a more accurate prediction model than an individual linear or nonlinear model for forecasting time series data originating from various applications. The linear autoregressive integrated moving average (ARIMA) and nonlinear artificial neural network (ANN) models are explored in this paper to devise a new hybrid ARIMA-ANN model for the prediction of time series data. Many of the hybrid ARIMA-ANN models which exist in the literature apply an ARIMA model to given time series data, consider the error between the original and the ARIMA-predicted data as a nonlinear component, and model it using an ANN in different ways. Though these models give predictions with higher accuracy than the individual models, there is scope for further improvement in the accuracy if the nature of the given time series is taken into account before applying the models. In the work described in this paper, the nature of volatility was explored using a moving-average filter, and then an ARIMA and an ANN model were suitably applied. Using a simulated data set and experimental data sets such as sunspot data, electricity price data, and stock market data, the proposed hybrid ARIMA-ANN model was applied along with individual ARIMA and ANN models and some existing hybrid ARIMA-ANN models. The results obtained from all of these data sets show that for both one-step-ahead and multistep-ahead forecasts, the proposed hybrid model has higher prediction accuracy. Small size photovoltaic modules can harvest enough energy to power many personal devices and wireless sensor nodes. The prediction of solar energy intake is possible thanks to the periodical availability of the sunlight and its cyclic behavior. Thus, smart and innovative power management strategies can take advantage from intake prediction algorithms to optimize the energy usage by keeping the system in low power state as long as possible. On the other hand, very accurate predictions need time and energy because of complex calculations, thus an algorithm that can provide the optimal trade-off between computational effort and accuracy is a breakthrough for systems with tight power constraints. In this paper we introduce an innovative, efficient and reliable solar prediction algorithm, the weather conditioned moving average (WCMA). The algorithm has been further enhanced to increase performance using a phase displacement regulator (PDR) which reduces the average error to less than 9.2 at a minimum energy cost. The proposed new algorithm compares favorably with several competing approaches. Real-time electricity pricing models can potentially lead to economic and environmental advantages compared to the current common flat rates. In particular, they can provide end users with the opportunity to reduce their electricity expenditures by responding to pricing that varies with different times of the day. However, recent studies have revealed that the lack of knowledge among users about how to respond to time-varying prices as well as the lack of effective building automation systems are two major barriers for fully utilizing the potential benefits of real-time pricing tariffs. We tackle these problems by proposing an optimal and automatic residential energy consumption scheduling framework which attempts to achieve a desired trade-off between minimizing the electricity payment and minimizing the waiting time for the operation of each appliance in household in presence of a real-time pricing tariff combined with inclining block rates. Our design requires minimum effort from the users and is based on simple linear programming computations. Moreover, we argue that any residential load control strategy in real-time electricity pricing environments requires price prediction capabilities. This is particularly true if the utility companies provide price information only one or two hours ahead of time. By applying a simple and efficient weighted average price prediction filter to the actual hourly-based price values used by the Illinois Power Company from January 2007 to December 2009, we obtain the optimal choices of the coefficients for each day of the week to be used by the price predictor filter. Simulation results show that the combination of the proposed energy consumption scheduling design and the price predictor filter leads to significant reduction not only in users' payments but also in the resulting peak-to-average ratio in load demand for various load scenarios. Therefore, the deployment of the proposed optimal energy consumption scheduling schemes is beneficial for both end users and utility companies. Time-series data is increasingly collected in many domains. One example is the smart electricity infrastructure, which generates huge volumes of such data from sources such as smart electricity meters. Although today these data are used for visualization and billing in mostly 15-min resolution, its original temporal resolution frequently is more fine-grained, e.g., seconds. This is useful for various analytical applications such as short-term forecasting, disaggregation and visualization. However, transmitting and storing huge amounts of such fine-grained data are prohibitively expensive in terms of storage space in many cases. In this article, we present a compression technique based on piecewise regression and two methods which describe the performance of the compression. Although our technique is a general approach for time-series compression, smart grids serve as our running example and as our evaluation scenario. Depending on the data and the use-case scenario, the technique compresses data by ratios of up to factor 5,000 while maintaining its usefulness for analytics. The proposed technique has outperformed related work and has been applied to three real-world energy datasets in different scenarios. Finally, we show that the proposed compression technique can be implemented in a state-of-the-art database management system. There is growing interest in the use of grid-level storage to smooth variations in supply that are likely to arise with an increased use of wind and solar energy. Energy arbitrage, the process of buying, storing, and selling electricity to exploit variations in electricity spot prices, is becoming an important way of paying for expensive investments into grid-level storage. Independent system operators such as the New York Independent System Operator NYISO require that battery storage operators place bids into an hour-ahead market although settlements may occur in increments as small as five minutes, which is considered near "real-time". The operator has to place these bids without knowing the energy level in the battery at the beginning of the hour and simultaneously accounting for the value of leftover energy at the end of the hour. The problem is formulated as a dynamic program. We describe and employ a convergent approximate dynamic programming ADP algorithm that exploits monotonicity of the value function to find a revenue-generating bidding policy; using optimal benchmarks, we empirically show the computational benefits of the algorithm. Furthermore, we propose a distribution-free variant of the ADP algorithm that does not require any knowledge of the distribution of the price process and makes no assumptions regarding a specific real-time price model. We demonstrate that a policy trained on historical real-time price data from the NYISO using this distribution-free approach is indeed effective. This paper presents a novel modeling and con- trol approach for the aggregation of large numbers of hetero- geneous thermostatically controlled loads, such as refrigera- tors, electric water heaters, and air conditioners, and their usage for Demand Response. Unlike traditional Demand Re- sponse methods that act on time scales of hours, this ap- proach is able to provide short-term (e.g., second-to-second) ancillary services, such as balancing and frequency control. A statistical modeling approach based on Markov Chains is used to describe the evolution of probability mass in a tem- perature state space. The Markov state transition matrix is identified using state information from the population of thermostatically controlled loads. A predictive controller is used to control the aggregate population of loads such that it tracks a signal. A simulation example shows the applicabil- ity of the approach to realistic systems, and includes a com- parison of control performance depending on available state information and controller parameterization. We consider the problem of forecasting the aggregate demand of a pool of price-responsive consumers of electricity. The response of the aggregate load to price is modeled by an optimization problem that is characterized by a set of marginal utility curves and minimum and maximum power consumption limits. The task of estimating these parameters is addressed using a generalized inverse optimization scheme that, in turn, requires solving a nonconvex mathematical program. We introduce a solution method that overcomes the nonconvexities by solving instead two linear problems with a penalty term, which is statistically adjusted by using a cross-validation algorithm. The proposed methodology is data-driven and leverages information from regressors, such as time and weather variables, to account for changes in the parameter estimates. The power load of a group of heating, ventilation, and air conditioning systems in buildings is simulated, and the results show that the aggregate demand of the group can be successfully captured by the proposed model, making it suitable for short-term forecasting purposes. We investigate the probabilistic forecasting of solar and wind power generation in connection with the Global Energy Forecasting Competition 2014. We use a voted ensemble of a quantile regression forest model and a stacked random forest–gradient boosting decision tree model to predict the probability distribution. The raw probabilities thus obtained need to be post-processed using isotonic regression in order to conform to the monotonic-increase attribute of probability distributions. The results show a great performance in terms of the weighted pinball loss, with the model achieving second place on the final competition leaderboard. Correctly anticipating load characteristics of low voltage level is getting increased interest by distribution network operators. Energy disaggregation could be one of the potential approaches to exploit the massive amount of smart meter data to fulfill the task. Proper individual home appliance modeling is critical to the performance of NILM. In this paper, a hierarchical hidden Markov model (HHMM) framework to model home appliances is proposed. This model aims to provide better representation for those appliances that have multiple built-in modes with distinct power consumption profiles, such as washing machines and dishwashers. The dynamic Bayesian network representation of such an appliance model is built. A forward–backward algorithm, which is based on the framework of expectation maximization, is formalized for the HHMM fitting process. Tests on publically available data show that the HHMM and proposed algorithm can effectively handle the modeling of appliances with multiple functional modes, as well as better representing a general type of appliances. A disaggregation test also demonstrates that the fitted HHMM can be easily applied to a general inference solver to outperform conventional hidden Markov model in the estimation of energy disaggregation. Abstract This paper provides a technique based on stochastic programming to optimally solve the wind power problem faced by the uncertainty. Uncertainties regarding the wind availability, market prices, and balancing energy needs are considered throughout the paper. The objective of this paper is to derive the best offering strategy for a wind power producer in a Short-Term electricity market, while limiting the risk of expected profit and required reserve due to wind speed forecast volatility. Risk aversion is explicitly modeled using the conditional value-at-risk methodology. ARIMA techniques are used to predict next-day electricity prices and wind speed forecast. For more performance the probability distribution function of the error between forecasted value and realized value to scenario generation is used. A realistic of numerical case studies demonstrates the interest and the effectiveness.